spb/wp29_uqo

1{"type":"atis-latch","atis":"","sessionId":"6a59845e-54b1-4582-86f5-752e1f4df7e4"}2{"type":"queue-operation","operation":"enqueue","timestamp":"2026-10-30T05:25:31.522Z","sessionId":"6a59845e-54b1-4582-86f5-752e1f4df7e4","content":"Begin"}3{"type":"queue-operation","operation":"dequeue","timestamp":"2026-10-30T05:25:31.525Z","sessionId":"6a59845e-54b1-4582-86f5-752e1f4df7e4"}4{"parentUuid":null,"isSidechain":false,"promptId":"214a8ece-2bbc-459c-aa1a-66062e2a4650","type":"user","message":{"role":"user","content":"Begin"},"uuid":"06a7506c-5ba0-47ce-9183-8320e0a70563","timestamp":"2026-10-30T05:25:31.528Z","permissionMode":"default","promptSource":"sdk","turnOrigin":"sdk","turnPosition":{"promptIndex":0,"turnIndex":1},"userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0288","sessionId":"6a59845e-54b1-4582-86f5-752e1f4df7e4","version":"2.1.314","gitBranch":"HEAD"}5{"type":"api-request-shape","sessionId":"6a59845e-54b1-4582-86f5-752e1f4df7e4","shapeHash":"48ce5b29cfebf0ef61214c91a04304198dbc6c218e84d6eb22b1863e5edbbfe3","timestamp":"2026-10-30T05:25:31.538Z","version":"2.1.314","shape":{"system":[{"type":"text","text":"You are Claude Code, Anthropic's official CLI for Claude, running within the Claude Agent SDK."},{"type":"text","text":"CWD: /Users/simon-pierreboucher/wp29/runs/RC0288\nDate: 2026-10-30\n\nYou are a research assistant executing one bounded scientific task inside the current working directory, which is your workspace.\n\nEnvironment facts (identical for every task):\n- The task statement is the file `TASK.md` in the workspace. It is the complete and only authoritative description of what is required: the scientific question, the inputs under `data/`, the required output files and their exact keys, and the information-availability rules.\n- There is no network access. Use the Python interpreter `~/wp29/venv/bin/python` (numpy, pandas, scipy, statsmodels, matplotlib are installed). Standard shell tools are available. Do not try to install packages.\n- Write every deliverable under `./out/`. The required deliverables are listed in `TASK.md`; at minimum they always include `out/results.json`, `out/report.md`, and a replication entry point `out/analysis.py` that regenerates `out/results.json` from `data/` when run with `~/wp29/venv/bin/python out/analysis.py` from the workspace root.\n- You are working alone and unattended; nobody will answer questions. Make reasonable, documented decisions.\n\nIntegrity rules (identical for every task):\n- Never fabricate numbers, data, results, or references. Every number in `out/results.json` and `out/report.md` must be produced by code you ran on the provided inputs.\n- Respect the information-availability rules in `TASK.md` exactly (for example, never use information that would not have been available at the stated decision time).\n- If part of the task cannot be completed as specified, say so explicitly in `out/report.md`, explain why, and still write every required file (use `null` for values you could not legitimately produce). An honest statement of a limitation is always preferred to a fabricated or guessed value.\n- Do not modify anything under `data/`.\n- When you are done, end with a brief plain-text summary of what you produced and any limitations.\n\nComplete the task described in `TASK.md` by following this research protocol. The protocol adds no scientific information to the task; it organizes how you work and what evidence you leave behind.\n\nPHASES (execute in order; a phase is eligible only when its dependencies are satisfied):\n\nP0 Scope and contract (depends on: nothing)\n- Read `TASK.md` completely. In `out/state/contract.md`, restate in your own words: the scientific question; the quantity or quantities to be estimated (the estimands) and the assumptions under which they are meaningful; every required output file and key; the information-availability rules; what would make the task impossible or the result invalid.\n- Write the initial phase ledger `out/state/phases.json`: a JSON object mapping each phase id (P0 … P5) to {\"status\": \"pending\"|\"running\"|\"passed\"|\"failed\"|\"blocked\"|\"stale\"|\"skipped_optional\", \"checks\": [], \"notes\": \"\"}. Keep it current as you work. A phase may only be set to \"passed\" when the checks listed for it in this protocol have actually been executed and their outcome recorded in \"checks\".\n\nP1 Data audit and provenance (depends on: P0)\n- Inspect every input file: compute and record its SHA-256, row and column counts, schema, units, date ranges, missing values, duplicates and anomalies in `out/state/data_audit.md`.\n- Keep an exclusion ledger in `out/exclusions.csv` (columns: step, rule, n_before, n_after, n_removed, rationale) for every filtering step you apply, so that n_raw − total removed = n_analysed.\n- Identify any input that violates the information-availability rules and decide, in writing, how you will handle it.\n\nP2 Baseline (depends on: P1)\n- Before any elaborate method, compute the simplest valid baseline that answers the question (for example a naïve or unconditional estimate), with the same evaluation rule used later. Record it in `out/results.json` under the keys the task requires, or under `baseline_*` keys when the task does not name them.\n\nP3 Method and computation (depends on: P2)\n- Implement the analysis in `out/analysis.py` so that it reads only `data/`, writes `out/results.json` deterministically (fix every random seed you use and record it), and prints nothing that contradicts the written results. Any figure or table must be generated by this script.\n\nP4 Verification (depends on: P3)\n- Re-run `out/analysis.py` from a clean state and confirm it regenerates `out/results.json` with identical values. Record the check in `out/state/phases.json` under P4.\n- Check internal consistency: identities that must hold (for example counts that must add up, standard errors that must be positive, confidence intervals that must contain their estimates, date orderings that must respect the information rules), units, and magnitudes against the baseline. Record every check and its outcome in `out/state/checks.md`. A failed check must be fixed or honestly reported, never silently ignored.\n\nP5 Reporting (depends on: P4)\n- Write `out/report.md` with the sections: Question; Data and exclusions; Method; Results; Uncertainty; Limitations and open issues. Every quantitative statement must cite the key of `out/results.json` it comes from.\n- Write `out/claims.json`: a list of the central claims of your report, each as {\"id\": \"C1\", \"text\": \"...\", \"results_keys\": [\"...\"], \"status\": \"supported\"|\"unsupported\"|\"not_evaluated\", \"caveat\": \"...\"}. Do not state a claim in the report that is not in this ledger.\n- Set the final statuses in `out/state/phases.json`.\n\nSTOPPING RULES\n- Stop and declare the task complete only when every phase P0–P5 is \"passed\" with recorded checks. If a phase is \"blocked\" (missing information, impossible requirement, violated availability rule), stop, set the status, explain in `out/report.md` (section Limitations and open issues) and still write every required file with `null` for values you could not legitimately produce.\n- Do not spend effort on extensions that the task does not require.\n\nRECOVERY RULES\n- Append a short dated entry to `out/state/log.md` after each phase transition and after any important decision, so that work can be resumed by reading `out/state/`.\n- If you discover that an upstream input or decision changed after a downstream phase passed, mark the downstream phases \"stale\" and redo them; do not reuse stale results.\n- If you are interrupted and later resumed, first read `out/state/` and continue from the last consistent state; never re-run expensive steps that already have recorded, unchanged outputs.\n\nEXECUTABLE GATES (additional, mandatory)\n- A read-only gate program is provided at `~/wp29/gates/pgar_gate.py`. Run it from the workspace root as `~/wp29/venv/bin/python ~/wp29/gates/pgar_gate.py .` It checks the output contract of `TASK.md` (required files, keys, types, finite values, report sections, the claims ledger, the phase ledger, the exclusion ledger) and reproducibility (it re-runs `out/analysis.py` in a temporary copy and compares the regenerated `out/results.json` with yours). It never checks scientific correctness and it contains no reference answers.\n- You must run the gate at the end of P4 and again at the end of P5, and record each run's receipt (the gate writes `out/state/gate_receipts/<timestamp>.json`) in `out/state/phases.json`. You may not declare the task complete while the latest gate receipt reports `\"passed\": false`. Fix the reported problems and re-run the gate.\n- The controller will run the same gate after your session ends. If it fails, you will be resumed with the gate output and asked to fix the problems; each such cycle consumes your remaining budget.\n"}],"tools":[{"name":"Bash","description":"execute shell commands","input_schema":{"$schema":"https://json-schema.org/draft/2020-12/schema","type":"object","properties":{"command":{"description":"The command to execute","type":"string"},"timeout":{"description":"Optional timeout in milliseconds (max 600000 for a foreground command)","type":"number"},"description":{"description":"Clear, concise description of what this command does in active voice. 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the user's own instructions about these lines, such as a CLAUDE.md or memory rule, take precedence over this reminder, but do not add attribution lines this reminder leaves out):\n- End git commit messages with:\nCo-Authored-By: Claude Opus 5.5 <noreply@anthropic.com>\n- End pull request descriptions with:\n🤖 Generated with [Claude Code](https://claude.com/claude-code)\n</system-reminder>"}],"renderedRole":"user","renderedBesideToolResult":true,"userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0288","sessionId":"6a59845e-54b1-4582-86f5-752e1f4df7e4","version":"2.1.314","gitBranch":"HEAD"}11{"type":"tools-used","tools":["Bash"],"sessionId":"6a59845e-54b1-4582-86f5-752e1f4df7e4"}12{"parentUuid":"2f7f0e36-4752-4c5f-b3b0-63c6ee86e6be","isSidechain":false,"message":{"model":"claude-opus-5-5","id":"msg_011CgXsAXqfsLYq8VRTa9fbW","type":"message","role":"assistant","content":[{"type":"thinking","thinking":"","signature":"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"}],"container":null,"stop_reason":"tool_use","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":4,"cache_creation_input_tokens":2603,"cache_read_input_tokens":1504,"output_tokens":131,"output_tokens_details":{"thinking_tokens":23},"server_tool_use":{"web_search_requests":0,"web_fetch_requests":0},"service_tier":"standard","cache_creation":{"ephemeral_1h_input_tokens":0,"ephemeral_5m_input_tokens":2603},"inference_geo":"global","iterations":[],"speed":"standard","fallback_credit":null},"input_transformations":[],"diagnostics":null,"context_management":null},"thinkingDurationMs":556,"thinkingDisplay":"updates","apiBlockIndex":0,"requestId":"req_011CgXsAXYK4yNErcF268RzN","requestRef":"0a8c6562-0f1f-4855-be4a-007d2196552c","requestedModel":"claude-opus-5-5","type":"assistant","uuid":"a6dff11c-1d9a-4d5b-acad-bb81154b3de4","timestamp":"2026-10-30T05:25:33.864Z","effort":"medium","perTurnEffort":"medium","userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0288","sessionId":"6a59845e-54b1-4582-86f5-752e1f4df7e4","version":"2.1.314","gitBranch":"HEAD"}13{"parentUuid":"a6dff11c-1d9a-4d5b-acad-bb81154b3de4","isSidechain":false,"message":{"model":"claude-opus-5-5","id":"msg_011CgXsAXqfsLYq8VRTa9fbW","type":"message","role":"assistant","content":[{"type":"tool_use","id":"toolu_01YJnYxDHuSqvxG8wXcQ8hMV","name":"Bash","input":{"command":"cat TASK.md; 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standard errors in parentheses) and a randomised evaluation of Rhode Island's RESEA programme (average treatment effects with 95% confidence intervals). The task is to extract specified point estimates, standard errors, confidence-interval bounds and sample counts **exactly as printed**, to attach to each value a locator (source id, table number, row label, column label), to derive two quantities that the sources do not print (a 95% confidence interval from a standard error, and a standard error from a confidence interval) while stating the convention used, and to recognise which requested quantity is not reported at all. The estimands are the printed numbers themselves; nothing is re-estimated.\n\n## Inputs\n- `data/sources/arxiv-2609.27732v1.txt` — extract of Armendariz (2026), *Do Drug Consumption Rooms Reduce Drug-Related Hospitalizations? Evidence from Switzerland*, arXiv:2609.27732v1 [econ.GN], CC BY 4.0; contains the abstract, Section 6 (Results, with Table 6 \"Overall Average Treatment Effect on the Treated (ATT)\" by buffer distance and Table 7 \"Overall ATT by Outcome Category and Buffer Zone\") and Appendix Table A19. Outcomes are log hospitalisation rates per 100,000 inhabitants; ATTs are in log points; the line below each coefficient row holds standard errors in parentheses; stars denote p < 0.01 (***), p < 0.05 (**), p < 0.10 (*).\n- `data/sources/arxiv-2606.14621v1.txt` — extract of Li, Pearson-Merkowitz & Yokum (2026), *Evaluating the Impact of Rhode Island's Self-Sustaining RESEA Program on Employment Outcomes*, arXiv:2606.14621v1 [econ.GN], CC BY 4.0; contains the abstract, Section 3 (Results, with Table 2: group means, treatment-control differences and average treatment effect (ATE) estimates with two-sided p-values and 95% confidence intervals) and Table 3 (ATE estimates under three estimators). Wages are annualised US dollars; reemployment is in percent / percentage points (pp); weeks on UI are weeks. Tables 2 and 3 were re-set in a one-row-per-outcome layout (see the extract header); values are verbatim.\n- `data/README.md` — provenance, retrieval times, licences and SHA-256 of the extracts.\n\n## Information-availability rules\n- The two extracts are the only admissible evidence; do not use remembered values from the papers or other versions.\n- \"As printed\" means the sign and the number of decimals shown in the table that the key names (e.g. `-0.431`, `0.210`, `17250` for \"17,250\"). Where a value appears in more than one table, use the table named in the key's definition.\n- Derived keys must be computed from the printed values of the named table with a stated convention (normal 1.96 or 2.0, or a t critical value); the convention must be written in `derivation_notes`.\n- A locator has the form `<source id> | Table <n> | row \"<row label>\" | column \"<column label>\"`; for derived keys, name the table the inputs came from and say \"derived\".\n- If a requested quantity is not printed in the source (and cannot be derived from printed quantities without additional assumptions), report `null` and say so in the locator (e.g. `not reported in arxiv-2609.27732v1`).\n\n## Required outputs\n- `out/results.json` with the keys (all numbers \"as printed\" unless marked derived):\n\n| key | type | unit | meaning |\n|---|---|---|---|\n| `dcr_att_5km` | number | log points | Table 6, row \"ATT\", column \"5 km\" |\n| `dcr_att_5km_se` | number | log points | Table 6, standard error of the 5 km ATT |\n| `dcr_att_3km` | number | log points | Table 6, row \"ATT\", column \"3 km\" |\n| `dcr_att_3km_se` | number | log points | Table 6, standard error of the 3 km ATT |\n| `dcr_obs_5km` | integer | region-years | Table 6, row \"Observations\", column \"5 km\" |\n| `dcr_narcotics_att_3km` | number | log points | Table 7, row \"Narcotics\", column \"3 km\" |\n| `dcr_narcotics_att_3km_se` | number | log points | Table 7, standard error of that cell |\n| `dcr_fatal_att_1km` | number | log points | Table 7, row \"Drug-related (fatal)\", column \"1 km\" |\n| `dcr_fatal_att_1km_se` | number | log points | Table 7, standard error of that cell |\n| `dcr_hiv_att_5km` | number | log points | Table 7, row \"HIV\", column \"5 km\" |\n| `dcr_hiv_att_5km_se` | number | log points | Table 7, standard error of that cell |\n| `dcr_hiv_att_5km_p_value` | number or null | — | the exact two-sided p-value of the HIV 5 km ATT if the source prints it; otherwise null |\n| `dcr_hiv_att_5km_significant_10pct` | boolean | — | whether the source marks the HIV 5 km ATT as significant at the 10% level |\n| `dcr_att_5km_ci_low`, `dcr_att_5km_ci_high` | number | log points | **derived**: 95% confidence interval of the Table 6 5 km ATT from its printed SE |\n| `ri_wage_ate` | number | USD per year | Table 2, row \"Wage\", column \"Average treatment effect estimate\", point estimate |\n| `ri_wage_ate_ci_low`, `ri_wage_ate_ci_high` | number | USD per year | Table 2, the 95% CI bounds printed for that ATE |\n| `ri_wage_ate_se_reported` | boolean | — | whether the source prints a standard error for the wage ATE |\n| `ri_wage_ate_se_derived` | number | USD per year | **derived**: standard error implied by the printed 95% CI (state the critical value used) |\n| `ri_control_mean_wage` | number | USD per year | Table 2, row \"Wage\", column \"Control\" (mean) |\n| `ri_reemployment_ate_pp` | number | percentage points | Table 2, row \"Reemployment\", ATE point estimate |\n| `ri_weeks_ui_ate` | number | weeks | Table 2, row \"Weeks on UI\", ATE point estimate (sign as printed: negative = fewer weeks) |\n| `ri_weeks_ui_ate_ci_low`, `ri_weeks_ui_ate_ci_high` | number | weeks | Table 2, the 95% CI bounds printed for that ATE (as printed, lower bound first) |\n| `locators` | object | — | one locator string per key above (except the two booleans) |\n| `derivation_notes` | object | — | {\"dcr_att_5km_ci\": \"<convention>\", \"ri_wage_ate_se\": \"<convention>\"} |\n| `task_id` | string | — | \"F4-conf-01\" |\n| `feasibility` | string | — | \"complete\", \"partial\" or \"infeasible\" — refers to the task as a whole |\n| `feasibility_reason` | string or null | — | explanation if not complete |\n| `data_issues` | array of {\"issue\",\"handling\"} | — | problems detected in the inputs (rendering defects, internal inconsistencies of a source, etc.) and how they were handled; empty list if none |\n| `n_input_rows` | object | lines / items | {file name: number of lines} for every file under `data/` that you read (text files: number of newline-terminated lines as reported by `wc -l`; JSON files: number of top-level items) |\n| `n_analysed` | integer or null | — | number of reported items (values, assertions, effects or references) you actually assessed |\n| `random_seed` | integer or null | — | null (no randomness is involved) |\n\n- `out/report.md` with the sections: Question; Data and exclusions; Method; Results; Uncertainty; Limitations and open issues. The Uncertainty section must say which reported uncertainties are standard errors and which are confidence intervals, and what the derived quantities assume.\n- `out/analysis.py` — running `~/wp29/venv/bin/python out/analysis.py` from the workspace root must regenerate `out/results.json` from `data/` only, deterministically (text parsing is acceptable; hard-coding the numbers is acceptable only if the script also prints the matched source lines).\n- `out/claims.json` — a list of the central claims of the report: [{\"id\": \"C1\", \"text\": \"...\", \"results_keys\": [\"...\"], \"status\": \"supported\"|\"unsupported\"|\"not_evaluated\", \"caveat\": \"...\"}].\n\n## Constraints\n- No network access. Python: `~/wp29/venv/bin/python` (numpy, pandas, scipy, statsmodels available; the standard library is sufficient for this task). Do not modify `data/`.\n- The source extracts under `data/sources/` are the **only** admissible evidence. Do not rely on memory of the papers or on other versions of them; if a quantity is not in the extract, it is not available.\n- Report numbers exactly as printed in the source (same sign, same number of decimals); do not round or rescale printed values unless the key definition asks for a transformation.\n- This task is designed to be completed in well under 20 minutes of work.\ntotal 40\ndrwxr-xr-x    6 simon-pierreboucher  staff   192 oct.  29 23:45 .\ndrwxr-xr-x  160 simon-pierreboucher  staff  5120 oct.  30 01:25 ..\n-rw-r--r--    1 simon-pierreboucher  staff   641 oct.  29 23:45 bundle.json\ndrwxr-xr-x    4 simon-pierreboucher  staff   128 oct.  29 23:45 data\n-rw-r--r--    1 simon-pierreboucher  staff  3334 oct.  29 23:45 gate_spec.json\n-rw-r--r--    1 simon-pierreboucher  staff  8459 oct.  29 23:45 TASK.md\n\n./data:\ntotal 8\ndrwxr-xr-x  4 simon-pierreboucher  staff   128 oct.  29 23:45 .\ndrwxr-xr-x  6 simon-pierreboucher  staff   192 oct.  29 23:45 ..\n-rw-r--r--  1 simon-pierreboucher  staff  1822 oct.  29 23:45 README.md\ndrwxr-xr-x  4 simon-pierreboucher  staff   128 oct.  29 23:45 sources\n\n./data/sources:\ntotal 64\ndrwxr-xr-x  4 simon-pierreboucher  staff    128 oct.  29 23:45 .\ndrwxr-xr-x  4 simon-pierreboucher  staff    128 oct.  29 23:45 ..\n-rw-r--r--  1 simon-pierreboucher  staff  13405 oct.  29 23:45 arxiv-2606.14621v1.txt\n-rw-r--r--  1 simon-pierreboucher  staff  12401 oct.  29 23:45 arxiv-2609.27732v1.txt\n(eval):1: no matches found: out/state/*","is_error":false}]},"uuid":"6992bfec-9a50-4712-9a9c-3676ccd1a11a","timestamp":"2026-10-30T05:25:34.617Z","toolUseResult":{"stdout":"# F4-conf-01 — Extracting treatment effects and their uncertainty from two published results tables\n\n## Scientific question\nTwo open-access studies report treatment effects with uncertainty: a staggered difference-in-differences study of drug consumption room (DCR) openings in Switzerland (log hospitalisation rates; standard errors in parentheses) and a randomised evaluation of Rhode Island's RESEA programme (average treatment effects with 95% confidence intervals). The task is to extract specified point estimates, standard errors, confidence-interval bounds and sample counts **exactly as printed**, to attach to each value a locator (source id, table number, row label, column label), to derive two quantities that the sources do not print (a 95% confidence interval from a standard error, and a standard error from a confidence interval) while stating the convention used, and to recognise which requested quantity is not reported at all. The estimands are the printed numbers themselves; nothing is re-estimated.\n\n## Inputs\n- `data/sources/arxiv-2609.27732v1.txt` — extract of Armendariz (2026), *Do Drug Consumption Rooms Reduce Drug-Related Hospitalizations? Evidence from Switzerland*, arXiv:2609.27732v1 [econ.GN], CC BY 4.0; contains the abstract, Section 6 (Results, with Table 6 \"Overall Average Treatment Effect on the Treated (ATT)\" by buffer distance and Table 7 \"Overall ATT by Outcome Category and Buffer Zone\") and Appendix Table A19. Outcomes are log hospitalisation rates per 100,000 inhabitants; ATTs are in log points; the line below each coefficient row holds standard errors in parentheses; stars denote p < 0.01 (***), p < 0.05 (**), p < 0.10 (*).\n- `data/sources/arxiv-2606.14621v1.txt` — extract of Li, Pearson-Merkowitz & Yokum (2026), *Evaluating the Impact of Rhode Island's Self-Sustaining RESEA Program on Employment Outcomes*, arXiv:2606.14621v1 [econ.GN], CC BY 4.0; contains the abstract, Section 3 (Results, with Table 2: group means, treatment-control differences and average treatment effect (ATE) estimates with two-sided p-values and 95% confidence intervals) and Table 3 (ATE estimates under three estimators). Wages are annualised US dollars; reemployment is in percent / percentage points (pp); weeks on UI are weeks. Tables 2 and 3 were re-set in a one-row-per-outcome layout (see the extract header); values are verbatim.\n- `data/README.md` — provenance, retrieval times, licences and SHA-256 of the extracts.\n\n## Information-availability rules\n- The two extracts are the only admissible evidence; do not use remembered values from the papers or other versions.\n- \"As printed\" means the sign and the number of decimals shown in the table that the key names (e.g. `-0.431`, `0.210`, `17250` for \"17,250\"). Where a value appears in more than one table, use the table named in the key's definition.\n- Derived keys must be computed from the printed values of the named table with a stated convention (normal 1.96 or 2.0, or a t critical value); the convention must be written in `derivation_notes`.\n- A locator has the form `<source id> | Table <n> | row \"<row label>\" | column \"<column label>\"`; for derived keys, name the table the inputs came from and say \"derived\".\n- If a requested quantity is not printed in the source (and cannot be derived from printed quantities without additional assumptions), report `null` and say so in the locator (e.g. `not reported in arxiv-2609.27732v1`).\n\n## Required outputs\n- `out/results.json` with the keys (all numbers \"as printed\" unless marked derived):\n\n| key | type | unit | meaning |\n|---|---|---|---|\n| `dcr_att_5km` | number | log points | Table 6, row \"ATT\", column \"5 km\" |\n| `dcr_att_5km_se` | number | log points | Table 6, standard error of the 5 km ATT |\n| `dcr_att_3km` | number | log points | Table 6, row \"ATT\", column \"3 km\" |\n| `dcr_att_3km_se` | number | log points | Table 6, standard error of the 3 km ATT |\n| `dcr_obs_5km` | integer | region-years | Table 6, row \"Observations\", column \"5 km\" |\n| `dcr_narcotics_att_3km` | number | log points | Table 7, row \"Narcotics\", column \"3 km\" |\n| `dcr_narcotics_att_3km_se` | number | log points | Table 7, standard error of that cell |\n| `dcr_fatal_att_1km` | number | log points | Table 7, row \"Drug-related (fatal)\", column \"1 km\" |\n| `dcr_fatal_att_1km_se` | number | log points | Table 7, standard error of that cell |\n| `dcr_hiv_att_5km` | number | log points | Table 7, row \"HIV\", column \"5 km\" |\n| `dcr_hiv_att_5km_se` | number | log points | Table 7, standard error of that cell |\n| `dcr_hiv_att_5km_p_value` | number or null | — | the exact two-sided p-value of the HIV 5 km ATT if the source prints it; otherwise null |\n| `dcr_hiv_att_5km_significant_10pct` | boolean | — | whether the source marks the HIV 5 km ATT as significant at the 10% level |\n| `dcr_att_5km_ci_low`, `dcr_att_5km_ci_high` | number | log points | **derived**: 95% confidence interval of the Table 6 5 km ATT from its printed SE |\n| `ri_wage_ate` | number | USD per year | Table 2, row \"Wage\", column \"Average treatment effect estimate\", point estimate |\n| `ri_wage_ate_ci_low`, `ri_wage_ate_ci_high` | number | USD per year | Table 2, the 95% CI bounds printed for that ATE |\n| `ri_wage_ate_se_reported` | boolean | — | whether the source prints a standard error for the wage ATE |\n| `ri_wage_ate_se_derived` | number | USD per year | **derived**: standard error implied by the printed 95% CI (state the critical value used) |\n| `ri_control_mean_wage` | number | USD per year | Table 2, row \"Wage\", column \"Control\" (mean) |\n| `ri_reemployment_ate_pp` | number | percentage points | Table 2, row \"Reemployment\", ATE point estimate |\n| `ri_weeks_ui_ate` | number | weeks | Table 2, row \"Weeks on UI\", ATE point estimate (sign as printed: negative = fewer weeks) |\n| `ri_weeks_ui_ate_ci_low`, `ri_weeks_ui_ate_ci_high` | number | weeks | Table 2, the 95% CI bounds printed for that ATE (as printed, lower bound first) |\n| `locators` | object | — | one locator string per key above (except the two booleans) |\n| `derivation_notes` | object | — | {\"dcr_att_5km_ci\": \"<convention>\", \"ri_wage_ate_se\": \"<convention>\"} |\n| `task_id` | string | — | \"F4-conf-01\" |\n| `feasibility` | string | — | \"complete\", \"partial\" or \"infeasible\" — refers to the task as a whole |\n| `feasibility_reason` | string or null | — | explanation if not complete |\n| `data_issues` | array of {\"issue\",\"handling\"} | — | problems detected in the inputs (rendering defects, internal inconsistencies of a source, etc.) and how they were handled; empty list if none |\n| `n_input_rows` | object | lines / items | {file name: number of lines} for every file under `data/` that you read (text files: number of newline-terminated lines as reported by `wc -l`; JSON files: number of top-level items) |\n| `n_analysed` | integer or null | — | number of reported items (values, assertions, effects or references) you actually assessed |\n| `random_seed` | integer or null | — | null (no randomness is involved) |\n\n- `out/report.md` with the sections: Question; Data and exclusions; Method; Results; Uncertainty; Limitations and open issues. The Uncertainty section must say which reported uncertainties are standard errors and which are confidence intervals, and what the derived quantities assume.\n- `out/analysis.py` — running `~/wp29/venv/bin/python out/analysis.py` from the workspace root must regenerate `out/results.json` from `data/` only, deterministically (text parsing is acceptable; hard-coding the numbers is acceptable only if the script also prints the matched source lines).\n- `out/claims.json` — a list of the central claims of the report: [{\"id\": \"C1\", \"text\": \"...\", \"results_keys\": [\"...\"], \"status\": \"supported\"|\"unsupported\"|\"not_evaluated\", \"caveat\": \"...\"}].\n\n## Constraints\n- No network access. Python: `~/wp29/venv/bin/python` (numpy, pandas, scipy, statsmodels available; the standard library is sufficient for this task). Do not modify `data/`.\n- The source extracts under `data/sources/` are the **only** admissible evidence. Do not rely on memory of the papers or on other versions of them; if a quantity is not in the extract, it is not available.\n- Report numbers exactly as printed in the source (same sign, same number of decimals); do not round or rescale printed values unless the key definition asks for a transformation.\n- This task is designed to be completed in well under 20 minutes of work.\ntotal 40\ndrwxr-xr-x    6 simon-pierreboucher  staff   192 oct.  29 23:45 .\ndrwxr-xr-x  160 simon-pierreboucher  staff  5120 oct.  30 01:25 ..\n-rw-r--r--    1 simon-pierreboucher  staff   641 oct.  29 23:45 bundle.json\ndrwxr-xr-x    4 simon-pierreboucher  staff   128 oct.  29 23:45 data\n-rw-r--r--    1 simon-pierreboucher  staff  3334 oct.  29 23:45 gate_spec.json\n-rw-r--r--    1 simon-pierreboucher  staff  8459 oct.  29 23:45 TASK.md\n\n./data:\ntotal 8\ndrwxr-xr-x  4 simon-pierreboucher  staff   128 oct.  29 23:45 .\ndrwxr-xr-x  6 simon-pierreboucher  staff   192 oct.  29 23:45 ..\n-rw-r--r--  1 simon-pierreboucher  staff  1822 oct.  29 23:45 README.md\ndrwxr-xr-x  4 simon-pierreboucher  staff   128 oct.  29 23:45 sources\n\n./data/sources:\ntotal 64\ndrwxr-xr-x  4 simon-pierreboucher  staff    128 oct.  29 23:45 .\ndrwxr-xr-x  4 simon-pierreboucher  staff    128 oct.  29 23:45 ..\n-rw-r--r--  1 simon-pierreboucher  staff  13405 oct.  29 23:45 arxiv-2606.14621v1.txt\n-rw-r--r--  1 simon-pierreboucher  staff  12401 oct.  29 23:45 arxiv-2609.27732v1.txt\n(eval):1: no matches found: out/state/*","stderr":"","interrupted":false,"isImage":false,"noOutputExpected":false},"sandboxed":false,"sourceToolAssistantUUID":"fbf71bd1-c6ba-470e-add2-861d42d03db2","permissionDecision":{"decision":"accept","source":"config","reasonType":"rule","ruleSource":"cliArg"},"userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0288","sessionId":"6a59845e-54b1-4582-86f5-752e1f4df7e4","version":"2.1.314","gitBranch":"HEAD"}15{"type":"api-request-blob","sessionId":"6a59845e-54b1-4582-86f5-752e1f4df7e4","hash":"e07330cc1d7fe7e6c5ebdfa5d2faff3283302d0022ed7510d2dc263a0bda3e4a","message":{"role":"assistant","content":[{"type":"thinking","thinking":"","signature":"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"},{"type":"tool_use","id":"toolu_01YJnYxDHuSqvxG8wXcQ8hMV","name":"Bash","input":{"command":"cat TASK.md; 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Each extract begins with a provenance header (title, authors, bibliographic record, retrieval URL and UTC, SHA-256 of the retrieved HTML, license, list of included and omitted sections). Only the excerpts needed for the task are reproduced; everything else is omitted. Tables are rendered one row per line with ` | ` between cells; in regression tables, the line following a coefficient row holds the standard errors in parentheses as printed in the original.\n\n| file | document | retrieval UTC | license | SHA-256 of the extract |\n|---|---|---|---|---|\n| `sources/arxiv-2609.27732v1.txt` | Armendariz, A. (2026). Do Drug Consumption Rooms Reduce Drug-Related Hospitalizations? Evidence from Switzerland. arXiv:2609.27732v1 [econ.GN]. https://arxiv.org/abs/2609.27732v1 (full text: https://arxiv.org/html/2609.27732v1) | 2026-10-30T03:23:18Z | CC BY 4.0 | ee2d444d2f78f2af2ca2cf69b59a61fcd7ac5e10ea29e8ddcd67917bf07d6a8b |\n| `sources/arxiv-2606.14621v1.txt` | Li, H. H., Pearson-Merkowitz, S., & Yokum, D. (2026). Evaluating the Impact of Rhode Island's Self-Sustaining Reemployment Services and Eligibility Assessment (RESEA) Program on Employment Outcomes. arXiv:2606.14621v1 [econ.GN]. https://arxiv.org/abs/2606.14621v1 (full text: https://arxiv.org/html/2606.14621v1) | 2026-10-30T03:23:18Z | CC BY 4.0 | 27c463c0a144d9d5a3916dc641f15b6029f6c68b5e574ad9074e7df1aeb0a238 |\n\nRights: arXiv e-prints are reproduced in part under the Creative Commons Attribution 4.0 licence shown on their arXiv abstract pages. No numbers were altered; where a table was re-set for legibility (RESEA Tables 2-3, London Table 3 header) the extract header says so.\n     1\tSOURCE EXTRACT — arxiv-2609.27732v1\n     2\tTitle: Do Drug Consumption Rooms Reduce Drug-Related Hospitalizations? Evidence from Switzerland\n     3\tAuthors: Ana Armendariz (University of St. Gallen)\n     4\tBibliographic record: arXiv:2609.27732v1 [econ.GN], submitted 23 Sep 2026 (version 1, the only version at retrieval). Abstract page: https://arxiv.org/abs/2609.27732v1\n     5\tRetrieved: https://arxiv.org/html/2609.27732v1 on 2026-10-30T03:23:18Z (SHA-256 of the retrieved HTML: 22b06050f4ff6e665aedd80561fa4031b1717867ecfeca71d1d2902ee1b09f52)\n     6\tLicense: CC BY 4.0 (license shown on the arXiv abstract page; https://creativecommons.org/licenses/by/4.0/). Reproduced in part with attribution.\n     7\tExtract contents: title, author, date and abstract; Section 6 'Results' in full (Figure notes, Table 6, Table 7, Section 6.1 'Mechanisms'); Appendix Table A19 with its note and the paragraph that discusses it.\n     8\tOmitted: Sections 1-5 and 7, the other appendix tables and figures, the references.\n     9\tRendering note: converted from the HTML rendering to plain text. Tables are rendered one row per line with \" | \" separating cells; in regression tables the line following a coefficient row holds the standard errors in parentheses, exactly as printed. Mathematical expressions appear as LaTeX source; non-breaking spaces were replaced by ordinary spaces. No numbers were altered.\n    10\t================================================================\n    11\t\n    12\t# Do Drug Consumption Rooms Reduce Drug-Related Hospitalizations? Evidence from Switzerland\n    13\t\n    14\tAna Armendariz*\n    15\t\n    16\tSeptember 23, 2026\n    17\t\n    18\tThis paper estimates the causal effect of drug consumption room (DCR) openings on drug-related hospitalizations in Switzerland. I exploit the staggered introduction of DCRs across Swiss cities between 1998 and 2022, using individual-level hospital records and a difference-in-differences estimator. DCR openings reduce drug-related hospitalization rates by roughly 35% within 5 km of a facility. The reduction is most pronounced for fatal outcomes, which fall by 35% while narcotics poisoning hospitalizations rise by a comparable amount, consistent with DCRs converting potentially fatal overdoses into non-fatal hospitalizations. Mental and behavioral disorder hospitalizations decline by 32% while estimates for Hepatitis and HIV are imprecise. The findings support the role of DCRs as an effective harm reduction strategy.\n    19\t\n    20\tKeywords: Drug Consumption Rooms, Harm Reduction, Hospitalizations, Drugs, Public Health, Causal Inference\n    21\t\n    22\t## 6 Results\n    23\t\n    24\tThis section reports the effect of DCR openings on the rate of drug-related hospitalizations per 100,000 inhabitants. I begin with the event-study estimates and then present the overall pooled ATT. I then disaggregate the overall outcome into its sub-outcome categories and report the same estimates for each.\n    25\t\n    26\tFigure 6: Event study estimates by buffer zone, \\pm 5-year window\n    27\t\n    28\tNote: The figure plots event-study coefficients from the doubly robust estimator of Callaway and Sant’Anna (2021) for regions within 5 km of a DCR opening. The comparison group consists of never-treated regions (608 control regions). The outcome is the log drug-related hospitalization rate per 100,000 inhabitants. The sample includes all staggered-treated MedStat regions within 5 km of a DCR that opened between 1998 and 2022 and all never-treated regions. The estimation uses a \\pm 5-year window relative to DCR opening, with t=-5 and t=+5 representing pooled endpoint bins that absorb all periods beyond the window. The reference period is a universal base period. Regions near DCRs that opened before 1998 (always-treated) are excluded through out. 95% confidence intervals are based on standard errors clustered at the MedStat region level. Results for the 1 km and 3 km buffer specifications are reported in Appendix Figure A17.\n    29\t\n    30\tFigure 6 presents the event study estimates for the 5 km buffer specifications. I focus on the 5 km buffer specification, as it includes the largest number of treated regions and yields the most precise estimates. I summarize dynamic effects over a symmetric five year window around DCR openings. This window avoids relying on distant leads and lags that are informed by only a small subset of adopters. Pre-treatment coefficients are small and statistically insignificant, supporting the parallel trends assumption. The post-treatment coefficients are consistently negative, indicating a reduction in drug-related hospitalizations following DCR openings. The estimates of the overall summary of ATTs based on calendar time aggregation of Callaway and Sant’Anna (2021) is around -35% reduction in hospitalizations due to drugs.\n    31\t\n    32\tTable 6: Overall Average Treatment Effect on the Treated (ATT)\n    33\t\n    34\t| 1 km | 3 km | 5 km\n    35\t\n    36\tATT | -0.399 | -0.283 | -0.431**\n    37\t\n    38\t| (0.412) | (0.279) | (0.210)\n    39\t\n    40\t% change | -32.9% | -24.7% | -35.0%\n    41\t\n    42\tTreated regions | 7 | 35 | 50\n    43\t\n    44\tControl regions | 608 | 608 | 608\n    45\t\n    46\tObservations | 16,175 | 16,875 | 17,250\n    47\t\n    48\tNote: The table reports the overall ATT estimated using the doubly robust Callaway and Sant’Anna (2021) estimator with never-treated controls and a universal base period. The outcome is the log drug-related hospitalization rate per 100,000 inhabitants. Columns differ in the geographic buffer used to define treated Medstat regions. The control group consists of 608 never-treated Medstat regions\n    49\tthroughout. Regions near DCRs that opened before 1998\n    50\t(always-treated) are excluded. Standard errors clustered at the Medstat region level in\n    51\tparentheses. Significance: {}^{***}p<0.01 , {}^{**}p<0.05 ,\n    52\t{}^{*}p<0.10 .\n    53\t\n    54\tTable 6 reports the pooled ATT for the 1 km, 3 km and 5 km buffer specification, aggregating the dynamic treatment effects into a single estimate averaged across all post-treatment periods and cohorts. The 5 km specification, yields an ATT of -0.431\n    55\tcorresponding to a 35% reduction in the drug-related hospitalization rate. This estimate is statistically significant at the 5% level. The 3 km and 1 km estimates point in the same direction but are less precise, due to the smaller number of treated regions.\n    56\t\n    57\tTo further explore the mechanisms through which DCR openings affect drug-related hospitalizations, I disaggregate the main outcome into five diagnostic\n    58\tsub-categories: (i) mental and behavioral disorders due to psychoactive substance use, (ii) viral hepatitis, (iii) HIV, (iv) poisoning by narcotics and psychodysleptics, and (v) fatal drug-related hospitalizations. Accidental poisoning is excluded due to the very small number of cases.\n    59\t\n    60\tFigure 7: Event study: Sub-outcome estimates,\n    61\twithin 5 km, \\pm 5-year window\n    62\t\n    63\tNote: The figure plots event-study coefficients from the doubly robust Callaway & Sant’Anna (2021) estimator for four diagnostic sub-categories of drug-related hospitalizations. Each panel uses a separate y -axis to accommodate differences in scale across outcomes. The sample includes all staggered-treated MedStat regions within 5 km of a\n    64\tDCR that opened between 1998 and 2022. The reference period is a universal base period. Regions near DCRs that opened before 1998 (always-treated) are excluded. 95% confidence intervals are based on standard errors clustered at the MedStat region level.\n    65\t\n    66\tTable 7: Overall ATT by Outcome Category and Buffer Zone\n    67\t\n    68\tOutcome | 1 km | 3 km | 5 km\n    69\t\n    70\tMental/Behavioral | -0.354 | -0.202 | -0.381*\n    71\t\n    72\t| (0.382) | (0.244) | (0.220)\n    73\t\n    74\tHepatitis | -0.270 | -0.413 | -0.406\n    75\t\n    76\t| (0.486) | (0.270) | (0.250)\n    77\t\n    78\tHIV | 0.063 | -0.239 | -0.340\n    79\t\n    80\t| (0.801) | (0.337) | (0.294)\n    81\t\n    82\tNarcotics | 1.186*** | 0.549*** | 0.390**\n    83\t\n    84\t| (0.288) | (0.187) | (0.165)\n    85\t\n    86\tDrug-related (fatal) | -1.207*** | -0.290 | -0.354**\n    87\t\n    88\t| (0.137) | (0.215) | (0.178)\n    89\t\n    90\tTreated regions | 7 | 35 | 50\n    91\t\n    92\tControl regions | 608 (never-treated) | |\n    93\t\n    94\tNote: Each cell reports the overall ATT from\n    95\tthe doubly robust Callaway & Sant’Anna (2021) estimator with\n    96\tnever-treated controls and a universal base period. Outcomes are\n    97\tlog hospitalization rates per 100,000 inhabitants. Sub-outcomes:\n    98\tmental and behavioral disorders (F11–F19), viral hepatitis (B15–B19),\n    99\tHIV disease (B20–B24), narcotics/psychodysleptics (T40). The 1 km\n   100\testimates are identified from a single treated region per cohort and\n   101\tshould be interpreted with caution. Regions near DCRs opened before\n   102\t1998 excluded. Standard errors clustered at the Medstat region level.\n   103\tSignificance: {}^{***}p<0.01 , {}^{**}p<0.05 , {}^{*}p<0.10 .\n   104\t\n   105\tFigure 7 and Table 7 repeat the estimation for the five diagnostic categories. From Table 7, the ATT estimates for hospitalizations due to mental and behavioral disorders are consistently negative and marginally significant at the 5 km buffer specification. Moreover, the estimates of hepatitis are also negative and of comparable magnitude but insignificant. However, the event-study path in Figure 7 shows a gradual decline that builds from year 3 onward, consistent with a slow-acting reduction in needle sharing. HIV estimates are insignificant and close to zero. Narcotics poisoning hospitalizations rise after DCR opening and remain positive across all buffers. Lastly, fatal drug-related hospitalizations move in the opposite direction, falling after opening the DCR. The opposing patterns in narcotics and fatal hospitalizations suggest that DCRs convert events that would otherwise be fatal into survivable hospital contacts.\n   106\t\n   107\t### 6.1 Mechanisms\n   108\t\n   109\tThe sub-outcome results point to two channels through which DCRs reduce drug-related hospitalizations. The first is overdose supervision. DCRs provide a medically supervised environment where staff can control the dose consumed, perform resuscitation, and call emergency services when an overdose occurs. When supervision prevents a death that would otherwise happen outside a hospital, the consequence is a simultaneous decline in fatal drug-related hospitalizations and a rise in non-fatal poisoning contacts, as events that would have ended in death instead end in a hospital visit. The results are consistent with this prediction. Drug-related fatal hospitalizations fall near DCRs, by approximately 30% at 5 km, while narcotics and psychodysleptics hospitalizations rise by 47% at 5 km. The opposing signs and spatial concentration of these two effects are consistent with a\n   110\tsubstitution from out-of-hospital mortality to in-hospital survival. Since I cannot observe overdose events that do not result in a hospital contact, this interpretation is suggestive rather than identified, but it is consistent with the clinical evidence on DCR-based overdose reversals\n   111\t(Kerr et al., 2017; Hedrich, 2004).\n   112\t\n   113\tThe second channel is harm reduction for blood-borne diseases. DCRs distribute sterile injection equipment, which reduces needle sharing and the transmission of viral hepatitis and HIV. Unlike overdose prevention, these effects are expected to emerge slowly, since disease progression and hospitalization take years to follow from initial infection. The event-study path for hepatitis is consistent with this lag. The estimates are close to zero in the first two post-treatment years and become progressively more negative from year 3 onward, reaching approximately 40% below baseline by year 5. HIV estimates are near zero through out the analysis, with wide confidence intervals. A longer panel and additional out-patient data would be needed to determine whether these trends continue and to get more precise estimates.\n   114\t\n   115\tA third potential channel is referral to treatment and social services. DCRs routinely connect users to addiction treatment, primary care, and housing support, and a sustained reduction in hospitalizations could partly reflect better long-term management of drug dependence. The regional hospitalization panel used in this paper does not contain information on treatment entry or service use, so I cannot separate this channel from the two described above. It remains a plausible additional mechanism that future work with information on treatment take up data could examine.\n   116\t\n   117\tTable A19: Robustness: Overall ATT Across Estimators — 5 km buffer, \\pm 5-year window\n   118\t\n   119\t| CS-DR | Sun & Abraham | Synthetic DiD | Stacked DiD\n   120\t\n   121\tATT | -0.431** | -0.438** | -0.362* | -0.149***\n   122\t\n   123\t| (0.218) | (0.208) | (0.205) | (0.038)\n   124\t\n   125\t% change | -35.0% | -35.5% | -30.3% | -13.9%\n   126\t\n   127\tTreated regions | 50 | | |\n   128\t\n   129\tControl regions | 608 | | |\n   130\t\n   131\tObservations | 17,250 | | |\n   132\t\n   133\tNote: All specifications use the 5 km buffer,\n   134\tnever-treated comparison regions (640 regions), and a \\pm 5-year window\n{\n  \"task_id\": \"F4-conf-01\",\n  \"required_files\": [\n    \"out/results.json\",\n    \"out/report.md\",\n    \"out/analysis.py\",\n    \"out/claims.json\"\n  ],\n  \"results_keys\": {\n    \"dcr_att_5km\": {\n      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Each extract begins with a provenance header (title, authors, bibliographic record, retrieval URL and UTC, SHA-256 of the retrieved HTML, license, list of included and omitted sections). Only the excerpts needed for the task are reproduced; everything else is omitted. Tables are rendered one row per line with ` | ` between cells; in regression tables, the line following a coefficient row holds the standard errors in parentheses as printed in the original.\n\n| file | document | retrieval UTC | license | SHA-256 of the extract |\n|---|---|---|---|---|\n| `sources/arxiv-2609.27732v1.txt` | Armendariz, A. (2026). Do Drug Consumption Rooms Reduce Drug-Related Hospitalizations? Evidence from Switzerland. arXiv:2609.27732v1 [econ.GN]. https://arxiv.org/abs/2609.27732v1 (full text: https://arxiv.org/html/2609.27732v1) | 2026-10-30T03:23:18Z | CC BY 4.0 | ee2d444d2f78f2af2ca2cf69b59a61fcd7ac5e10ea29e8ddcd67917bf07d6a8b |\n| `sources/arxiv-2606.14621v1.txt` | Li, H. H., Pearson-Merkowitz, S., & Yokum, D. (2026). Evaluating the Impact of Rhode Island's Self-Sustaining Reemployment Services and Eligibility Assessment (RESEA) Program on Employment Outcomes. arXiv:2606.14621v1 [econ.GN]. https://arxiv.org/abs/2606.14621v1 (full text: https://arxiv.org/html/2606.14621v1) | 2026-10-30T03:23:18Z | CC BY 4.0 | 27c463c0a144d9d5a3916dc641f15b6029f6c68b5e574ad9074e7df1aeb0a238 |\n\nRights: arXiv e-prints are reproduced in part under the Creative Commons Attribution 4.0 licence shown on their arXiv abstract pages. No numbers were altered; where a table was re-set for legibility (RESEA Tables 2-3, London Table 3 header) the extract header says so.\n     1\tSOURCE EXTRACT — arxiv-2609.27732v1\n     2\tTitle: Do Drug Consumption Rooms Reduce Drug-Related Hospitalizations? Evidence from Switzerland\n     3\tAuthors: Ana Armendariz (University of St. Gallen)\n     4\tBibliographic record: arXiv:2609.27732v1 [econ.GN], submitted 23 Sep 2026 (version 1, the only version at retrieval). Abstract page: https://arxiv.org/abs/2609.27732v1\n     5\tRetrieved: https://arxiv.org/html/2609.27732v1 on 2026-10-30T03:23:18Z (SHA-256 of the retrieved HTML: 22b06050f4ff6e665aedd80561fa4031b1717867ecfeca71d1d2902ee1b09f52)\n     6\tLicense: CC BY 4.0 (license shown on the arXiv abstract page; https://creativecommons.org/licenses/by/4.0/). Reproduced in part with attribution.\n     7\tExtract contents: title, author, date and abstract; Section 6 'Results' in full (Figure notes, Table 6, Table 7, Section 6.1 'Mechanisms'); Appendix Table A19 with its note and the paragraph that discusses it.\n     8\tOmitted: Sections 1-5 and 7, the other appendix tables and figures, the references.\n     9\tRendering note: converted from the HTML rendering to plain text. Tables are rendered one row per line with \" | \" separating cells; in regression tables the line following a coefficient row holds the standard errors in parentheses, exactly as printed. Mathematical expressions appear as LaTeX source; non-breaking spaces were replaced by ordinary spaces. No numbers were altered.\n    10\t================================================================\n    11\t\n    12\t# Do Drug Consumption Rooms Reduce Drug-Related Hospitalizations? Evidence from Switzerland\n    13\t\n    14\tAna Armendariz*\n    15\t\n    16\tSeptember 23, 2026\n    17\t\n    18\tThis paper estimates the causal effect of drug consumption room (DCR) openings on drug-related hospitalizations in Switzerland. I exploit the staggered introduction of DCRs across Swiss cities between 1998 and 2022, using individual-level hospital records and a difference-in-differences estimator. DCR openings reduce drug-related hospitalization rates by roughly 35% within 5 km of a facility. The reduction is most pronounced for fatal outcomes, which fall by 35% while narcotics poisoning hospitalizations rise by a comparable amount, consistent with DCRs converting potentially fatal overdoses into non-fatal hospitalizations. Mental and behavioral disorder hospitalizations decline by 32% while estimates for Hepatitis and HIV are imprecise. The findings support the role of DCRs as an effective harm reduction strategy.\n    19\t\n    20\tKeywords: Drug Consumption Rooms, Harm Reduction, Hospitalizations, Drugs, Public Health, Causal Inference\n    21\t\n    22\t## 6 Results\n    23\t\n    24\tThis section reports the effect of DCR openings on the rate of drug-related hospitalizations per 100,000 inhabitants. I begin with the event-study estimates and then present the overall pooled ATT. I then disaggregate the overall outcome into its sub-outcome categories and report the same estimates for each.\n    25\t\n    26\tFigure 6: Event study estimates by buffer zone, \\pm 5-year window\n    27\t\n    28\tNote: The figure plots event-study coefficients from the doubly robust estimator of Callaway and Sant’Anna (2021) for regions within 5 km of a DCR opening. The comparison group consists of never-treated regions (608 control regions). The outcome is the log drug-related hospitalization rate per 100,000 inhabitants. The sample includes all staggered-treated MedStat regions within 5 km of a DCR that opened between 1998 and 2022 and all never-treated regions. The estimation uses a \\pm 5-year window relative to DCR opening, with t=-5 and t=+5 representing pooled endpoint bins that absorb all periods beyond the window. The reference period is a universal base period. Regions near DCRs that opened before 1998 (always-treated) are excluded through out. 95% confidence intervals are based on standard errors clustered at the MedStat region level. Results for the 1 km and 3 km buffer specifications are reported in Appendix Figure A17.\n    29\t\n    30\tFigure 6 presents the event study estimates for the 5 km buffer specifications. I focus on the 5 km buffer specification, as it includes the largest number of treated regions and yields the most precise estimates. I summarize dynamic effects over a symmetric five year window around DCR openings. This window avoids relying on distant leads and lags that are informed by only a small subset of adopters. Pre-treatment coefficients are small and statistically insignificant, supporting the parallel trends assumption. The post-treatment coefficients are consistently negative, indicating a reduction in drug-related hospitalizations following DCR openings. The estimates of the overall summary of ATTs based on calendar time aggregation of Callaway and Sant’Anna (2021) is around -35% reduction in hospitalizations due to drugs.\n    31\t\n    32\tTable 6: Overall Average Treatment Effect on the Treated (ATT)\n    33\t\n    34\t| 1 km | 3 km | 5 km\n    35\t\n    36\tATT | -0.399 | -0.283 | -0.431**\n    37\t\n    38\t| (0.412) | (0.279) | (0.210)\n    39\t\n    40\t% change | -32.9% | -24.7% | -35.0%\n    41\t\n    42\tTreated regions | 7 | 35 | 50\n    43\t\n    44\tControl regions | 608 | 608 | 608\n    45\t\n    46\tObservations | 16,175 | 16,875 | 17,250\n    47\t\n    48\tNote: The table reports the overall ATT estimated using the doubly robust Callaway and Sant’Anna (2021) estimator with never-treated controls and a universal base period. The outcome is the log drug-related hospitalization rate per 100,000 inhabitants. Columns differ in the geographic buffer used to define treated Medstat regions. The control group consists of 608 never-treated Medstat regions\n    49\tthroughout. Regions near DCRs that opened before 1998\n    50\t(always-treated) are excluded. Standard errors clustered at the Medstat region level in\n    51\tparentheses. Significance: {}^{***}p<0.01 , {}^{**}p<0.05 ,\n    52\t{}^{*}p<0.10 .\n    53\t\n    54\tTable 6 reports the pooled ATT for the 1 km, 3 km and 5 km buffer specification, aggregating the dynamic treatment effects into a single estimate averaged across all post-treatment periods and cohorts. The 5 km specification, yields an ATT of -0.431\n    55\tcorresponding to a 35% reduction in the drug-related hospitalization rate. This estimate is statistically significant at the 5% level. The 3 km and 1 km estimates point in the same direction but are less precise, due to the smaller number of treated regions.\n    56\t\n    57\tTo further explore the mechanisms through which DCR openings affect drug-related hospitalizations, I disaggregate the main outcome into five diagnostic\n    58\tsub-categories: (i) mental and behavioral disorders due to psychoactive substance use, (ii) viral hepatitis, (iii) HIV, (iv) poisoning by narcotics and psychodysleptics, and (v) fatal drug-related hospitalizations. Accidental poisoning is excluded due to the very small number of cases.\n    59\t\n    60\tFigure 7: Event study: Sub-outcome estimates,\n    61\twithin 5 km, \\pm 5-year window\n    62\t\n    63\tNote: The figure plots event-study coefficients from the doubly robust Callaway & Sant’Anna (2021) estimator for four diagnostic sub-categories of drug-related hospitalizations. Each panel uses a separate y -axis to accommodate differences in scale across outcomes. The sample includes all staggered-treated MedStat regions within 5 km of a\n    64\tDCR that opened between 1998 and 2022. The reference period is a universal base period. Regions near DCRs that opened before 1998 (always-treated) are excluded. 95% confidence intervals are based on standard errors clustered at the MedStat region level.\n    65\t\n    66\tTable 7: Overall ATT by Outcome Category and Buffer Zone\n    67\t\n    68\tOutcome | 1 km | 3 km | 5 km\n    69\t\n    70\tMental/Behavioral | -0.354 | -0.202 | -0.381*\n    71\t\n    72\t| (0.382) | (0.244) | (0.220)\n    73\t\n    74\tHepatitis | -0.270 | -0.413 | -0.406\n    75\t\n    76\t| (0.486) | (0.270) | (0.250)\n    77\t\n    78\tHIV | 0.063 | -0.239 | -0.340\n    79\t\n    80\t| (0.801) | (0.337) | (0.294)\n    81\t\n    82\tNarcotics | 1.186*** | 0.549*** | 0.390**\n    83\t\n    84\t| (0.288) | (0.187) | (0.165)\n    85\t\n    86\tDrug-related (fatal) | -1.207*** | -0.290 | -0.354**\n    87\t\n    88\t| (0.137) | (0.215) | (0.178)\n    89\t\n    90\tTreated regions | 7 | 35 | 50\n    91\t\n    92\tControl regions | 608 (never-treated) | |\n    93\t\n    94\tNote: Each cell reports the overall ATT from\n    95\tthe doubly robust Callaway & Sant’Anna (2021) estimator with\n    96\tnever-treated controls and a universal base period. Outcomes are\n    97\tlog hospitalization rates per 100,000 inhabitants. Sub-outcomes:\n    98\tmental and behavioral disorders (F11–F19), viral hepatitis (B15–B19),\n    99\tHIV disease (B20–B24), narcotics/psychodysleptics (T40). The 1 km\n   100\testimates are identified from a single treated region per cohort and\n   101\tshould be interpreted with caution. Regions near DCRs opened before\n   102\t1998 excluded. Standard errors clustered at the Medstat region level.\n   103\tSignificance: {}^{***}p<0.01 , {}^{**}p<0.05 , {}^{*}p<0.10 .\n   104\t\n   105\tFigure 7 and Table 7 repeat the estimation for the five diagnostic categories. From Table 7, the ATT estimates for hospitalizations due to mental and behavioral disorders are consistently negative and marginally significant at the 5 km buffer specification. Moreover, the estimates of hepatitis are also negative and of comparable magnitude but insignificant. However, the event-study path in Figure 7 shows a gradual decline that builds from year 3 onward, consistent with a slow-acting reduction in needle sharing. HIV estimates are insignificant and close to zero. Narcotics poisoning hospitalizations rise after DCR opening and remain positive across all buffers. Lastly, fatal drug-related hospitalizations move in the opposite direction, falling after opening the DCR. The opposing patterns in narcotics and fatal hospitalizations suggest that DCRs convert events that would otherwise be fatal into survivable hospital contacts.\n   106\t\n   107\t### 6.1 Mechanisms\n   108\t\n   109\tThe sub-outcome results point to two channels through which DCRs reduce drug-related hospitalizations. The first is overdose supervision. DCRs provide a medically supervised environment where staff can control the dose consumed, perform resuscitation, and call emergency services when an overdose occurs. When supervision prevents a death that would otherwise happen outside a hospital, the consequence is a simultaneous decline in fatal drug-related hospitalizations and a rise in non-fatal poisoning contacts, as events that would have ended in death instead end in a hospital visit. The results are consistent with this prediction. Drug-related fatal hospitalizations fall near DCRs, by approximately 30% at 5 km, while narcotics and psychodysleptics hospitalizations rise by 47% at 5 km. The opposing signs and spatial concentration of these two effects are consistent with a\n   110\tsubstitution from out-of-hospital mortality to in-hospital survival. Since I cannot observe overdose events that do not result in a hospital contact, this interpretation is suggestive rather than identified, but it is consistent with the clinical evidence on DCR-based overdose reversals\n   111\t(Kerr et al., 2017; Hedrich, 2004).\n   112\t\n   113\tThe second channel is harm reduction for blood-borne diseases. DCRs distribute sterile injection equipment, which reduces needle sharing and the transmission of viral hepatitis and HIV. Unlike overdose prevention, these effects are expected to emerge slowly, since disease progression and hospitalization take years to follow from initial infection. The event-study path for hepatitis is consistent with this lag. The estimates are close to zero in the first two post-treatment years and become progressively more negative from year 3 onward, reaching approximately 40% below baseline by year 5. HIV estimates are near zero through out the analysis, with wide confidence intervals. A longer panel and additional out-patient data would be needed to determine whether these trends continue and to get more precise estimates.\n   114\t\n   115\tA third potential channel is referral to treatment and social services. DCRs routinely connect users to addiction treatment, primary care, and housing support, and a sustained reduction in hospitalizations could partly reflect better long-term management of drug dependence. The regional hospitalization panel used in this paper does not contain information on treatment entry or service use, so I cannot separate this channel from the two described above. It remains a plausible additional mechanism that future work with information on treatment take up data could examine.\n   116\t\n   117\tTable A19: Robustness: Overall ATT Across Estimators — 5 km buffer, \\pm 5-year window\n   118\t\n   119\t| CS-DR | Sun & Abraham | Synthetic DiD | Stacked DiD\n   120\t\n   121\tATT | -0.431** | -0.438** | -0.362* | -0.149***\n   122\t\n   123\t| (0.218) | (0.208) | (0.205) | (0.038)\n   124\t\n   125\t% change | -35.0% | -35.5% | -30.3% | -13.9%\n   126\t\n   127\tTreated regions | 50 | | |\n   128\t\n   129\tControl regions | 608 | | |\n   130\t\n   131\tObservations | 17,250 | | |\n   132\t\n   133\tNote: All specifications use the 5 km buffer,\n   134\tnever-treated comparison regions (640 regions), and a \\pm 5-year window\n{\n  \"task_id\": \"F4-conf-01\",\n  \"required_files\": [\n    \"out/results.json\",\n    \"out/report.md\",\n    \"out/analysis.py\",\n    \"out/claims.json\"\n  ],\n  \"results_keys\": {\n    \"dcr_att_5km\": {\n      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    1\tSOURCE EXTRACT — arxiv-2606.14621v1\n     2\tTitle: Evaluating the Impact of Rhode Island's Self-Sustaining Reemployment Services and Eligibility Assessment (RESEA) Program on Employment Outcomes\n     3\tAuthors: Harrison H. Li; Shanna Pearson-Merkowitz; David Yokum\n     4\tBibliographic record: arXiv:2606.14621v1 [econ.GN], submitted 12 Jun 2026 (version 1, the only version at retrieval). Abstract page: https://arxiv.org/abs/2606.14621v1\n     5\tRetrieved: https://arxiv.org/html/2606.14621v1 on 2026-10-30T03:23:18Z (SHA-256 of the retrieved HTML: f98fb1b39edd2107051cddf1da34aa8cf19e74e04d60bf9305a88cf966417da1)\n     6\tLicense: CC BY 4.0 (license shown on the arXiv abstract page; https://creativecommons.org/licenses/by/4.0/). Reproduced in part with attribution.\n     7\tExtract contents: title, authors and abstract; Section 3 'Results' (Table 2 with caption, the results paragraphs, Section 3.1 'Cost-benefit analysis'); from Section 4.1, Table 3 with its caption and the paragraph that discusses it.\n     8\tOmitted: Sections 1-2, Table 1, Section 3.2 (heterogeneous effects), the rest of Section 4, the references and the appendix.\n     9\tRendering note: converted from the HTML rendering to plain text. Tables are rendered one row per line with \" | \" separating cells; in regression tables the line following a coefficient row holds the standard errors in parentheses, exactly as printed. Mathematical expressions appear as LaTeX source; non-breaking spaces were replaced by ordinary spaces. No numbers were altered.\n    10\tEditorial note: Tables 2 and 3 were re-set in a one-row-per-outcome layout because the HTML rendering split each outcome over three rows and detached the p-values from their columns; the p-values and confidence intervals are attached to the column they belong to in the original (first p-value = treatment-control difference, second = average treatment effect).\n    11\t================================================================\n    12\t\n    13\t# Evaluating the Impact of Rhode Island's Self-Sustaining Reemployment Services and Eligibility Assessment (RESEA) Program on Employment Outcomes\n    14\t\n    15\tHarrison H. Li (Harvey Mudd College), Shanna Pearson-Merkowitz (University of Maryland, College Park), David Yokum (University of North Carolina at Chapel Hill)\n    16\t\n    17\t###### Abstract\n    18\t\n    19\tProlonged unemployment carries serious economic, health, and wellbeing costs. With federal support, most U.S. states now operate a Reemployment Services and Eligibility Assessment (RESEA) program to help Unemployment Insurance (UI) claimants return to work faster. We report results from a large (N = 23,549) preregistered randomized controlled trial (RCT) evaluating Rhode Island’s RESEA program from February 2022 to September 2023. We estimate that selection into the program increased annualized wages by $1,153, increased reemployment by 1.5 percentage points, and reduced UI duration by nearly two weeks. The vast majority of these wage and reemployment effects appeared within two quarters of claimants’ first pay dates and persisted through at least the following year, and we estimate that each dollar spent on the program saved the state $2.64. Using causal forests, a machine learning technique for estimating heterogeneous treatment effects (HTE), we also conduct an exploratory analysis to investigate if there are differential effects of selection into the RESEA program. We find that all participants experienced positive wage benefits from RESEA selection, with particularly large effects for older and lower-income workers. Finally, we improve upon prior RESEA evaluations by explicitly controlling for the week of treatment assignment — a methodological refinement absent from several existing RCTs of job-training programs that is important to eliminate confounding bias. We also discuss ways to harvest precision gains from baseline covariate adjustment without introducing large-sample bias.\n    20\t\n    21\t## 3 Results\n    22\t\n    23\t[Table 1 (pre-treatment characteristics) omitted from this extract.]\n    24\t\n    25\t[Table 2 — re-set by the extract editor from the HTML rendering, in which each outcome occupied three table rows; all values are verbatim. Columns: Outcome | Treatment | Control | Treatment-control difference | Average treatment effect estimate]\n    26\t\n    27\tOutcome | Treatment | Control | Treatment-control difference | Average treatment effect estimate\n    28\tWage | $33,073 (SD: 36,749) | $33,043 (SD: 36,231) | $29; p=0.95; 95% CI: [-903, 962] | $1,153; p=0.022; 95% CI: [166, 2,141]\n    29\tReemployment | 75.2% | 75.0% | 0.26 pp; p=0.64; 95% CI: [-0.84, 1.37] | 1.47 pp; p=0.013; 95% CI: [0.32, 2.62]\n    30\tWeeks on UI | 15.2 weeks (SD: 9.3) | 17.3 weeks (SD: 9.4) | -2.10 weeks; p<0.001; 95% CI: [-2.34, -1.86] | -1.99 weeks; p<0.001; 95% CI: [-2.24, -1.75]\n    31\tShort-term wage | $23,776 (SD: 31,948) | $23,897 (SD: 32,293) | -$121; p=0.77; 95% CI: [-941, 700] | $1,195; p=0.007; 95% CI: [326, 2,064]\n    32\tShort-term reemployment | 61.4% | 61.6% | -0.18 pp; p=0.77; 95% CI: [-1.43, 1.06] | 1.66 pp; p=0.012; 95% CI: [0.37, 2.96]\n    33\tShort-term weeks on UI | 14.2 weeks (SD: 8.4) | 15.9 weeks (SD: 8.3) | -1.70 weeks; p<0.001; 95% CI: [-1.92, -1.49] | -1.77 weeks; p<0.001; 95% CI: [-1.99, -1.55]\n    34\t\n    35\tTable 2: The average values of our long-term and short-term outcomes for the aggregate treatment and control groups in the study cohort, along with information about the differences in these averages between the treatment and control groups and our average treatment effect estimates. For the average outcomes, standard deviations are given in parentheses for the quantitative outcomes (wage and weeks on UI). For the treatment-control differences and the average treatment effect estimates, we provide two-sided p -values and 95% confidence intervals based on Welch’s two-sample t -test and the methodology described in the main text, respectively. The two rightmost columns illustrate how direct comparisons of outcome averages between the aggregate treatment and control groups can lead to significantly biased causal conclusions.\n    36\t\n    37\tOverall, we find that RESEA selection led to a statistically significant improvement in all three of the preregistered outcomes. RESEA selection led to a wage outcome that was $1,153 higher annually (95% CI: [$166, $2,141]; p=0.022 ), a 3.5% increase relative to the mean wage outcome of $33,043 in the control group (Table 2). There was also an estimated 1.47 percentage point increase in reemployment (95% CI: [0.32, 2.62]; p=0.013 ) and a 1.99 week reduction in the number of weeks on UI (95% CI: [1.75 weeks, 2.24 weeks]; p<0.001 ) due to RESEA selection. The latter is an 11.5% decrease compared to the mean control group weeks on UI outcome of 17.3 weeks. All three of these results remain statistically significant after correcting for multiple testing using Holm’s method to keep the familywise error rate below 5 percent.\n    38\t\n    39\tThe results for the short-term outcomes are substantively similar to those for the longer-term outcomes (Table 2). We estimate RESEA selection leads to a short-term wage outcome that is $1,195 higher (95% CI: [$326, $2,064]; p = 0.007) on an annualized basis and a 1.66 percentage point higher rate of short-term reemployment (95% CI: [0.37, 2.96]; p = 0.012). Given that the magnitudes of these causal estimates are very similar to those for the corresponding long-term outcomes, this suggests that the full impact of RESEA selection on wages and reemployment appears quickly by the second quarter after randomization, and persists for at least five quarters. We also find that those selected for RESEA spend 1.77 fewer weeks on UI in the benefit year of their claim (95% CI: [1.55 weeks, 1.99 weeks]; p ¡ 0.001). As this is a bit smaller than the effect size of 1.99 weeks estimated for the long term weeks on UI outcome, which includes an additional 26 weeks beyond the benefit year, we see some directional evidence that RESEA selection may also reduce the time spent on UI in the following year, suggesting that it may be helping claimants find employment that lasts longer, thereby reducing repetitive use of UI benefits.\n    40\t\n    41\tAs noted earlier, wages and reemployment are artificially zero-inflated due to missing out-of-state wage data. The Rhode Island job market is tightly integrated with those of the neighboring states of Massachusetts and Connecticut; the 2016-2020 5-Year American Community Survey found that nearly 16 percent of Rhode Island workers 16 years and older commuted out of state, mainly to Massachusetts. We note, however, that this missingness should not affect the directional validity of our causal analysis. This is because randomization ensures that this missingness is balanced between the treatment and control groups (within each block), unless RESEA selection itself differentially helps or hinders finding employment out of state or self-employment. To investigate this, we examined the presence of individuals who failed to exhaust their UI benefits in the benefit year of their claim while having $0 in reported RI wages for all of the five complete quarters after randomization. There were 3,325 claimants falling into this group, including 1,618 individuals (13.7%) in the aggregate control group and 1,707 individuals (14.6%) in the aggregate treatment group. After controlling for randomization week, this difference in proportions was not significant ( p = 0.55). The lack of statistical significance on this test with our large sample size along with the fact that the Rhode Island job database focuses overwhelmingly on jobs offered by Rhode Island employers suggests that RESEA selection does not differentially assist in finding out-of-state employment or self-employment.\n    42\t\n    43\t### 3.1 Cost-benefit analysis\n    44\t\n    45\tWe find that the decrease in time spent drawing UI benefits due to RESEA selection corresponds to a considerable cost savings to the state from paying out a smaller amount of UI benefits. We estimate that the average individual selected for RESEA during the study period drew $676 less (95% CI: [$563, $789], p<0.001 ) in UI benefits during the same benefit year of their claim than they would have if they were not selected for RESEA. This is a 9.9 percent reduction relative to the control group average of $6,839 in UI benefits drawn during the benefit year of the claim. Multiplying the estimate by the total number of individuals selected for RESEA in the study ( n=11,700 ) suggests that as a result of the program, the state paid out about $7.9 million fewer in UI benefits to claimants randomized during the study period over the same benefit year as their UI claims. This is much higher than the total expenditures of $2.99 million for the RESEA program over the study period, representing $2.64 in savings for the state budget for every $1 spent on the program.\n    46\t\n    47\t### 4.1 Methodological lessons (excerpt)\n    48\t\n    49\t[Table 3 — re-set by the extract editor from the HTML rendering; all values are verbatim. Columns: Outcome | Difference in means | Covariate adjusted | Fixed effects regression]\n    50\t\n    51\tOutcome | Difference in means | Covariate adjusted | Fixed effects regression\n    52\tWage | $1,153; p=0.022; 95% CI: [166, 2,141] | $1,137; p=0.014; 95% CI: [228, 2,047] | $1,100; p=0.027; 95% CI: [127, 2,073]\n    53\tReemployment | 1.47 pp; p=0.013; 95% CI: [0.32, 2.62] | 1.48 pp; p=0.012; 95% CI: [0.32, 2.63] | 1.49 pp; p=0.011; 95% CI: [0.34, 2.64]\n    54\tWeeks on UI | -1.99 wks; p<0.001; 95% CI: [-2.24, -1.75] | -1.93 wks; p<0.001; 95% CI: [-2.18, -1.68] | -1.97 wks; p<0.001; 95% CI: [-2.22, -1.72]\n    55\t\n    56\tTable 3: A comparison of the ITT estimates, two-sided p -values, and 95% confidence intervals for our primary outcomes from three different methodologies: the preregistered analysis based on eq. (1), the covariate adjusted analysis based on eq. (2), and the fixed effects regression in eq. (3). The leftmost column based on the preregistered analysis duplicates the rightmost column in Table 2 for ease of comparison.\n    57\t\n    58\tIf we directly compared treatment group and control group outcome averages without accounting for the week of randomization, the results would suggest RESEA selection had no detectable average effect on the wage and reemployment outcomes (column 4, Table 2). However, such an analysis is potentially biased under the experimental design because it does not respect the blocked randomization, which makes the week of randomization a known confounder. For example, weeks with a larger number of UI applicants, which had a higher proportion of control group individuals, tended to have lower-earning applicants (Fig. 7), thereby inflating the average wage outcome in the control group relative to the treatment group. This is critical because out of the thirteen publically available studies of RESEA-related employment assistance programs rated by CLEAR as having high causal evidence, six used block randomization like we do, yet only two of these (Black, Smith, Berger, & Noel., 2002; Klerman et al., 2019) properly adjusted for the block. Three others among these six studies used regression or matching methods in an attempt to control for observed demographic variables but not the block (Poe-Yamagata et al., 2011; Michaelides et al., 2012; Manoli et al., 2018), while the remaining study (Michaelides & Mueser, 2018) fit a linear regression model with the block indicators as fixed effects, as did two other more recent evaluations in Nevada (Michaelides & Mian, 2021; Brigandi et al., 2024) . These fixed effects regressions amount to estimating the coefficient in the regression model\n      10 data/README.md\n      58 data/sources/arxiv-2606.14621v1.txt\n     134 data/sources/arxiv-2609.27732v1.txt\n     202 total\n16ebdff389eeaee9508fe81c4edc43690f31f3463c469680c814134086ef9929  data/README.md\n27c463c0a144d9d5a3916dc641f15b6029f6c68b5e574ad9074e7df1aeb0a238  data/sources/arxiv-2606.14621v1.txt\nee2d444d2f78f2af2ca2cf69b59a61fcd7ac5e10ea29e8ddcd67917bf07d6a8b  data/sources/arxiv-2609.27732v1.txt","is_error":false}]},"uuid":"c436b49b-7e38-420d-9e60-569f47720a0b","timestamp":"2026-10-30T05:25:37.478Z","toolUseResult":{"stdout":"     1\tSOURCE EXTRACT — arxiv-2606.14621v1\n     2\tTitle: Evaluating the Impact of Rhode Island's Self-Sustaining Reemployment Services and Eligibility Assessment (RESEA) Program on Employment Outcomes\n     3\tAuthors: Harrison H. Li; Shanna Pearson-Merkowitz; David Yokum\n     4\tBibliographic record: arXiv:2606.14621v1 [econ.GN], submitted 12 Jun 2026 (version 1, the only version at retrieval). Abstract page: https://arxiv.org/abs/2606.14621v1\n     5\tRetrieved: https://arxiv.org/html/2606.14621v1 on 2026-10-30T03:23:18Z (SHA-256 of the retrieved HTML: f98fb1b39edd2107051cddf1da34aa8cf19e74e04d60bf9305a88cf966417da1)\n     6\tLicense: CC BY 4.0 (license shown on the arXiv abstract page; https://creativecommons.org/licenses/by/4.0/). Reproduced in part with attribution.\n     7\tExtract contents: title, authors and abstract; Section 3 'Results' (Table 2 with caption, the results paragraphs, Section 3.1 'Cost-benefit analysis'); from Section 4.1, Table 3 with its caption and the paragraph that discusses it.\n     8\tOmitted: Sections 1-2, Table 1, Section 3.2 (heterogeneous effects), the rest of Section 4, the references and the appendix.\n     9\tRendering note: converted from the HTML rendering to plain text. Tables are rendered one row per line with \" | \" separating cells; in regression tables the line following a coefficient row holds the standard errors in parentheses, exactly as printed. Mathematical expressions appear as LaTeX source; non-breaking spaces were replaced by ordinary spaces. No numbers were altered.\n    10\tEditorial note: Tables 2 and 3 were re-set in a one-row-per-outcome layout because the HTML rendering split each outcome over three rows and detached the p-values from their columns; the p-values and confidence intervals are attached to the column they belong to in the original (first p-value = treatment-control difference, second = average treatment effect).\n    11\t================================================================\n    12\t\n    13\t# Evaluating the Impact of Rhode Island's Self-Sustaining Reemployment Services and Eligibility Assessment (RESEA) Program on Employment Outcomes\n    14\t\n    15\tHarrison H. Li (Harvey Mudd College), Shanna Pearson-Merkowitz (University of Maryland, College Park), David Yokum (University of North Carolina at Chapel Hill)\n    16\t\n    17\t###### Abstract\n    18\t\n    19\tProlonged unemployment carries serious economic, health, and wellbeing costs. With federal support, most U.S. states now operate a Reemployment Services and Eligibility Assessment (RESEA) program to help Unemployment Insurance (UI) claimants return to work faster. We report results from a large (N = 23,549) preregistered randomized controlled trial (RCT) evaluating Rhode Island’s RESEA program from February 2022 to September 2023. We estimate that selection into the program increased annualized wages by $1,153, increased reemployment by 1.5 percentage points, and reduced UI duration by nearly two weeks. The vast majority of these wage and reemployment effects appeared within two quarters of claimants’ first pay dates and persisted through at least the following year, and we estimate that each dollar spent on the program saved the state $2.64. Using causal forests, a machine learning technique for estimating heterogeneous treatment effects (HTE), we also conduct an exploratory analysis to investigate if there are differential effects of selection into the RESEA program. We find that all participants experienced positive wage benefits from RESEA selection, with particularly large effects for older and lower-income workers. Finally, we improve upon prior RESEA evaluations by explicitly controlling for the week of treatment assignment — a methodological refinement absent from several existing RCTs of job-training programs that is important to eliminate confounding bias. We also discuss ways to harvest precision gains from baseline covariate adjustment without introducing large-sample bias.\n    20\t\n    21\t## 3 Results\n    22\t\n    23\t[Table 1 (pre-treatment characteristics) omitted from this extract.]\n    24\t\n    25\t[Table 2 — re-set by the extract editor from the HTML rendering, in which each outcome occupied three table rows; all values are verbatim. Columns: Outcome | Treatment | Control | Treatment-control difference | Average treatment effect estimate]\n    26\t\n    27\tOutcome | Treatment | Control | Treatment-control difference | Average treatment effect estimate\n    28\tWage | $33,073 (SD: 36,749) | $33,043 (SD: 36,231) | $29; p=0.95; 95% CI: [-903, 962] | $1,153; p=0.022; 95% CI: [166, 2,141]\n    29\tReemployment | 75.2% | 75.0% | 0.26 pp; p=0.64; 95% CI: [-0.84, 1.37] | 1.47 pp; p=0.013; 95% CI: [0.32, 2.62]\n    30\tWeeks on UI | 15.2 weeks (SD: 9.3) | 17.3 weeks (SD: 9.4) | -2.10 weeks; p<0.001; 95% CI: [-2.34, -1.86] | -1.99 weeks; p<0.001; 95% CI: [-2.24, -1.75]\n    31\tShort-term wage | $23,776 (SD: 31,948) | $23,897 (SD: 32,293) | -$121; p=0.77; 95% CI: [-941, 700] | $1,195; p=0.007; 95% CI: [326, 2,064]\n    32\tShort-term reemployment | 61.4% | 61.6% | -0.18 pp; p=0.77; 95% CI: [-1.43, 1.06] | 1.66 pp; p=0.012; 95% CI: [0.37, 2.96]\n    33\tShort-term weeks on UI | 14.2 weeks (SD: 8.4) | 15.9 weeks (SD: 8.3) | -1.70 weeks; p<0.001; 95% CI: [-1.92, -1.49] | -1.77 weeks; p<0.001; 95% CI: [-1.99, -1.55]\n    34\t\n    35\tTable 2: The average values of our long-term and short-term outcomes for the aggregate treatment and control groups in the study cohort, along with information about the differences in these averages between the treatment and control groups and our average treatment effect estimates. For the average outcomes, standard deviations are given in parentheses for the quantitative outcomes (wage and weeks on UI). For the treatment-control differences and the average treatment effect estimates, we provide two-sided p -values and 95% confidence intervals based on Welch’s two-sample t -test and the methodology described in the main text, respectively. The two rightmost columns illustrate how direct comparisons of outcome averages between the aggregate treatment and control groups can lead to significantly biased causal conclusions.\n    36\t\n    37\tOverall, we find that RESEA selection led to a statistically significant improvement in all three of the preregistered outcomes. RESEA selection led to a wage outcome that was $1,153 higher annually (95% CI: [$166, $2,141]; p=0.022 ), a 3.5% increase relative to the mean wage outcome of $33,043 in the control group (Table 2). There was also an estimated 1.47 percentage point increase in reemployment (95% CI: [0.32, 2.62]; p=0.013 ) and a 1.99 week reduction in the number of weeks on UI (95% CI: [1.75 weeks, 2.24 weeks]; p<0.001 ) due to RESEA selection. The latter is an 11.5% decrease compared to the mean control group weeks on UI outcome of 17.3 weeks. All three of these results remain statistically significant after correcting for multiple testing using Holm’s method to keep the familywise error rate below 5 percent.\n    38\t\n    39\tThe results for the short-term outcomes are substantively similar to those for the longer-term outcomes (Table 2). We estimate RESEA selection leads to a short-term wage outcome that is $1,195 higher (95% CI: [$326, $2,064]; p = 0.007) on an annualized basis and a 1.66 percentage point higher rate of short-term reemployment (95% CI: [0.37, 2.96]; p = 0.012). Given that the magnitudes of these causal estimates are very similar to those for the corresponding long-term outcomes, this suggests that the full impact of RESEA selection on wages and reemployment appears quickly by the second quarter after randomization, and persists for at least five quarters. We also find that those selected for RESEA spend 1.77 fewer weeks on UI in the benefit year of their claim (95% CI: [1.55 weeks, 1.99 weeks]; p ¡ 0.001). As this is a bit smaller than the effect size of 1.99 weeks estimated for the long term weeks on UI outcome, which includes an additional 26 weeks beyond the benefit year, we see some directional evidence that RESEA selection may also reduce the time spent on UI in the following year, suggesting that it may be helping claimants find employment that lasts longer, thereby reducing repetitive use of UI benefits.\n    40\t\n    41\tAs noted earlier, wages and reemployment are artificially zero-inflated due to missing out-of-state wage data. The Rhode Island job market is tightly integrated with those of the neighboring states of Massachusetts and Connecticut; the 2016-2020 5-Year American Community Survey found that nearly 16 percent of Rhode Island workers 16 years and older commuted out of state, mainly to Massachusetts. We note, however, that this missingness should not affect the directional validity of our causal analysis. This is because randomization ensures that this missingness is balanced between the treatment and control groups (within each block), unless RESEA selection itself differentially helps or hinders finding employment out of state or self-employment. To investigate this, we examined the presence of individuals who failed to exhaust their UI benefits in the benefit year of their claim while having $0 in reported RI wages for all of the five complete quarters after randomization. There were 3,325 claimants falling into this group, including 1,618 individuals (13.7%) in the aggregate control group and 1,707 individuals (14.6%) in the aggregate treatment group. After controlling for randomization week, this difference in proportions was not significant ( p = 0.55). The lack of statistical significance on this test with our large sample size along with the fact that the Rhode Island job database focuses overwhelmingly on jobs offered by Rhode Island employers suggests that RESEA selection does not differentially assist in finding out-of-state employment or self-employment.\n    42\t\n    43\t### 3.1 Cost-benefit analysis\n    44\t\n    45\tWe find that the decrease in time spent drawing UI benefits due to RESEA selection corresponds to a considerable cost savings to the state from paying out a smaller amount of UI benefits. We estimate that the average individual selected for RESEA during the study period drew $676 less (95% CI: [$563, $789], p<0.001 ) in UI benefits during the same benefit year of their claim than they would have if they were not selected for RESEA. This is a 9.9 percent reduction relative to the control group average of $6,839 in UI benefits drawn during the benefit year of the claim. Multiplying the estimate by the total number of individuals selected for RESEA in the study ( n=11,700 ) suggests that as a result of the program, the state paid out about $7.9 million fewer in UI benefits to claimants randomized during the study period over the same benefit year as their UI claims. This is much higher than the total expenditures of $2.99 million for the RESEA program over the study period, representing $2.64 in savings for the state budget for every $1 spent on the program.\n    46\t\n    47\t### 4.1 Methodological lessons (excerpt)\n    48\t\n    49\t[Table 3 — re-set by the extract editor from the HTML rendering; all values are verbatim. Columns: Outcome | Difference in means | Covariate adjusted | Fixed effects regression]\n    50\t\n    51\tOutcome | Difference in means | Covariate adjusted | Fixed effects regression\n    52\tWage | $1,153; p=0.022; 95% CI: [166, 2,141] | $1,137; p=0.014; 95% CI: [228, 2,047] | $1,100; p=0.027; 95% CI: [127, 2,073]\n    53\tReemployment | 1.47 pp; p=0.013; 95% CI: [0.32, 2.62] | 1.48 pp; p=0.012; 95% CI: [0.32, 2.63] | 1.49 pp; p=0.011; 95% CI: [0.34, 2.64]\n    54\tWeeks on UI | -1.99 wks; p<0.001; 95% CI: [-2.24, -1.75] | -1.93 wks; p<0.001; 95% CI: [-2.18, -1.68] | -1.97 wks; p<0.001; 95% CI: [-2.22, -1.72]\n    55\t\n    56\tTable 3: A comparison of the ITT estimates, two-sided p -values, and 95% confidence intervals for our primary outcomes from three different methodologies: the preregistered analysis based on eq. (1), the covariate adjusted analysis based on eq. (2), and the fixed effects regression in eq. (3). The leftmost column based on the preregistered analysis duplicates the rightmost column in Table 2 for ease of comparison.\n    57\t\n    58\tIf we directly compared treatment group and control group outcome averages without accounting for the week of randomization, the results would suggest RESEA selection had no detectable average effect on the wage and reemployment outcomes (column 4, Table 2). However, such an analysis is potentially biased under the experimental design because it does not respect the blocked randomization, which makes the week of randomization a known confounder. For example, weeks with a larger number of UI applicants, which had a higher proportion of control group individuals, tended to have lower-earning applicants (Fig. 7), thereby inflating the average wage outcome in the control group relative to the treatment group. This is critical because out of the thirteen publically available studies of RESEA-related employment assistance programs rated by CLEAR as having high causal evidence, six used block randomization like we do, yet only two of these (Black, Smith, Berger, & Noel., 2002; Klerman et al., 2019) properly adjusted for the block. Three others among these six studies used regression or matching methods in an attempt to control for observed demographic variables but not the block (Poe-Yamagata et al., 2011; Michaelides et al., 2012; Manoli et al., 2018), while the remaining study (Michaelides & Mueser, 2018) fit a linear regression model with the block indicators as fixed effects, as did two other more recent evaluations in Nevada (Michaelides & Mian, 2021; Brigandi et al., 2024) . These fixed effects regressions amount to estimating the coefficient in the regression model\n      10 data/README.md\n      58 data/sources/arxiv-2606.14621v1.txt\n     134 data/sources/arxiv-2609.27732v1.txt\n     202 total\n16ebdff389eeaee9508fe81c4edc43690f31f3463c469680c814134086ef9929  data/README.md\n27c463c0a144d9d5a3916dc641f15b6029f6c68b5e574ad9074e7df1aeb0a238  data/sources/arxiv-2606.14621v1.txt\nee2d444d2f78f2af2ca2cf69b59a61fcd7ac5e10ea29e8ddcd67917bf07d6a8b  data/sources/arxiv-2609.27732v1.txt","stderr":"","interrupted":false,"isImage":false,"noOutputExpected":false},"sandboxed":false,"sourceToolAssistantUUID":"1a20149a-d47e-4b56-98d3-3508a9b7e677","permissionDecision":{"decision":"accept","source":"config","reasonType":"rule","ruleSource":"cliArg"},"userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0288","sessionId":"6a59845e-54b1-4582-86f5-752e1f4df7e4","version":"2.1.314","gitBranch":"HEAD"}23{"type":"api-request-blob","sessionId":"6a59845e-54b1-4582-86f5-752e1f4df7e4","hash":"724ee6bb1b3056f18311fb8996fb75d06a57e0251a98f42a9b084ff43e9f1d9f","message":{"role":"assistant","content":[{"type":"tool_use","id":"toolu_01PitwsbKogGVWnknRfqPqoQ","name":"Bash","input":{"command":"cat data/README.md; cat -n data/sources/arxiv-2609.27732v1.txt; cat gate_spec.json bundle.json","description":"Read README, DCR source, gate spec"}},{"type":"tool_use","id":"toolu_01HFaMqJiZRjvTCV6o2ARQrq","name":"Bash","input":{"command":"cat -n data/sources/arxiv-2606.14621v1.txt; wc -l data/README.md data/sources/*; shasum -a 256 data/README.md data/sources/*","description":"Read RESEA source, counts, hashes"}}]}}24{"type":"api-request-blob","sessionId":"6a59845e-54b1-4582-86f5-752e1f4df7e4","hash":"0fd5a4c0d0b92b2a166814578c650a301e325d45023d6bf170e2fe574db59eb6","message":{"role":"user","content":[{"tool_use_id":"toolu_01PitwsbKogGVWnknRfqPqoQ","type":"tool_result","content":"# Data provenance — F4-conf-01\n\nAll files under `data/sources/` are plain-text extracts of publicly accessible documents, prepared by the task designer on 2026-10-30 (UTC). Each extract begins with a provenance header (title, authors, bibliographic record, retrieval URL and UTC, SHA-256 of the retrieved HTML, license, list of included and omitted sections). Only the excerpts needed for the task are reproduced; everything else is omitted. Tables are rendered one row per line with ` | ` between cells; in regression tables, the line following a coefficient row holds the standard errors in parentheses as printed in the original.\n\n| file | document | retrieval UTC | license | SHA-256 of the extract |\n|---|---|---|---|---|\n| `sources/arxiv-2609.27732v1.txt` | Armendariz, A. (2026). Do Drug Consumption Rooms Reduce Drug-Related Hospitalizations? Evidence from Switzerland. arXiv:2609.27732v1 [econ.GN]. https://arxiv.org/abs/2609.27732v1 (full text: https://arxiv.org/html/2609.27732v1) | 2026-10-30T03:23:18Z | CC BY 4.0 | ee2d444d2f78f2af2ca2cf69b59a61fcd7ac5e10ea29e8ddcd67917bf07d6a8b |\n| `sources/arxiv-2606.14621v1.txt` | Li, H. H., Pearson-Merkowitz, S., & Yokum, D. (2026). Evaluating the Impact of Rhode Island's Self-Sustaining Reemployment Services and Eligibility Assessment (RESEA) Program on Employment Outcomes. arXiv:2606.14621v1 [econ.GN]. https://arxiv.org/abs/2606.14621v1 (full text: https://arxiv.org/html/2606.14621v1) | 2026-10-30T03:23:18Z | CC BY 4.0 | 27c463c0a144d9d5a3916dc641f15b6029f6c68b5e574ad9074e7df1aeb0a238 |\n\nRights: arXiv e-prints are reproduced in part under the Creative Commons Attribution 4.0 licence shown on their arXiv abstract pages. No numbers were altered; where a table was re-set for legibility (RESEA Tables 2-3, London Table 3 header) the extract header says so.\n     1\tSOURCE EXTRACT — arxiv-2609.27732v1\n     2\tTitle: Do Drug Consumption Rooms Reduce Drug-Related Hospitalizations? Evidence from Switzerland\n     3\tAuthors: Ana Armendariz (University of St. Gallen)\n     4\tBibliographic record: arXiv:2609.27732v1 [econ.GN], submitted 23 Sep 2026 (version 1, the only version at retrieval). Abstract page: https://arxiv.org/abs/2609.27732v1\n     5\tRetrieved: https://arxiv.org/html/2609.27732v1 on 2026-10-30T03:23:18Z (SHA-256 of the retrieved HTML: 22b06050f4ff6e665aedd80561fa4031b1717867ecfeca71d1d2902ee1b09f52)\n     6\tLicense: CC BY 4.0 (license shown on the arXiv abstract page; https://creativecommons.org/licenses/by/4.0/). Reproduced in part with attribution.\n     7\tExtract contents: title, author, date and abstract; Section 6 'Results' in full (Figure notes, Table 6, Table 7, Section 6.1 'Mechanisms'); Appendix Table A19 with its note and the paragraph that discusses it.\n     8\tOmitted: Sections 1-5 and 7, the other appendix tables and figures, the references.\n     9\tRendering note: converted from the HTML rendering to plain text. Tables are rendered one row per line with \" | \" separating cells; in regression tables the line following a coefficient row holds the standard errors in parentheses, exactly as printed. Mathematical expressions appear as LaTeX source; non-breaking spaces were replaced by ordinary spaces. No numbers were altered.\n    10\t================================================================\n    11\t\n    12\t# Do Drug Consumption Rooms Reduce Drug-Related Hospitalizations? Evidence from Switzerland\n    13\t\n    14\tAna Armendariz*\n    15\t\n    16\tSeptember 23, 2026\n    17\t\n    18\tThis paper estimates the causal effect of drug consumption room (DCR) openings on drug-related hospitalizations in Switzerland. I exploit the staggered introduction of DCRs across Swiss cities between 1998 and 2022, using individual-level hospital records and a difference-in-differences estimator. DCR openings reduce drug-related hospitalization rates by roughly 35% within 5 km of a facility. The reduction is most pronounced for fatal outcomes, which fall by 35% while narcotics poisoning hospitalizations rise by a comparable amount, consistent with DCRs converting potentially fatal overdoses into non-fatal hospitalizations. Mental and behavioral disorder hospitalizations decline by 32% while estimates for Hepatitis and HIV are imprecise. The findings support the role of DCRs as an effective harm reduction strategy.\n    19\t\n    20\tKeywords: Drug Consumption Rooms, Harm Reduction, Hospitalizations, Drugs, Public Health, Causal Inference\n    21\t\n    22\t## 6 Results\n    23\t\n    24\tThis section reports the effect of DCR openings on the rate of drug-related hospitalizations per 100,000 inhabitants. I begin with the event-study estimates and then present the overall pooled ATT. I then disaggregate the overall outcome into its sub-outcome categories and report the same estimates for each.\n    25\t\n    26\tFigure 6: Event study estimates by buffer zone, \\pm 5-year window\n    27\t\n    28\tNote: The figure plots event-study coefficients from the doubly robust estimator of Callaway and Sant’Anna (2021) for regions within 5 km of a DCR opening. The comparison group consists of never-treated regions (608 control regions). The outcome is the log drug-related hospitalization rate per 100,000 inhabitants. The sample includes all staggered-treated MedStat regions within 5 km of a DCR that opened between 1998 and 2022 and all never-treated regions. The estimation uses a \\pm 5-year window relative to DCR opening, with t=-5 and t=+5 representing pooled endpoint bins that absorb all periods beyond the window. The reference period is a universal base period. Regions near DCRs that opened before 1998 (always-treated) are excluded through out. 95% confidence intervals are based on standard errors clustered at the MedStat region level. Results for the 1 km and 3 km buffer specifications are reported in Appendix Figure A17.\n    29\t\n    30\tFigure 6 presents the event study estimates for the 5 km buffer specifications. I focus on the 5 km buffer specification, as it includes the largest number of treated regions and yields the most precise estimates. I summarize dynamic effects over a symmetric five year window around DCR openings. This window avoids relying on distant leads and lags that are informed by only a small subset of adopters. Pre-treatment coefficients are small and statistically insignificant, supporting the parallel trends assumption. The post-treatment coefficients are consistently negative, indicating a reduction in drug-related hospitalizations following DCR openings. The estimates of the overall summary of ATTs based on calendar time aggregation of Callaway and Sant’Anna (2021) is around -35% reduction in hospitalizations due to drugs.\n    31\t\n    32\tTable 6: Overall Average Treatment Effect on the Treated (ATT)\n    33\t\n    34\t| 1 km | 3 km | 5 km\n    35\t\n    36\tATT | -0.399 | -0.283 | -0.431**\n    37\t\n    38\t| (0.412) | (0.279) | (0.210)\n    39\t\n    40\t% change | -32.9% | -24.7% | -35.0%\n    41\t\n    42\tTreated regions | 7 | 35 | 50\n    43\t\n    44\tControl regions | 608 | 608 | 608\n    45\t\n    46\tObservations | 16,175 | 16,875 | 17,250\n    47\t\n    48\tNote: The table reports the overall ATT estimated using the doubly robust Callaway and Sant’Anna (2021) estimator with never-treated controls and a universal base period. The outcome is the log drug-related hospitalization rate per 100,000 inhabitants. Columns differ in the geographic buffer used to define treated Medstat regions. The control group consists of 608 never-treated Medstat regions\n    49\tthroughout. Regions near DCRs that opened before 1998\n    50\t(always-treated) are excluded. Standard errors clustered at the Medstat region level in\n    51\tparentheses. Significance: {}^{***}p<0.01 , {}^{**}p<0.05 ,\n    52\t{}^{*}p<0.10 .\n    53\t\n    54\tTable 6 reports the pooled ATT for the 1 km, 3 km and 5 km buffer specification, aggregating the dynamic treatment effects into a single estimate averaged across all post-treatment periods and cohorts. The 5 km specification, yields an ATT of -0.431\n    55\tcorresponding to a 35% reduction in the drug-related hospitalization rate. This estimate is statistically significant at the 5% level. The 3 km and 1 km estimates point in the same direction but are less precise, due to the smaller number of treated regions.\n    56\t\n    57\tTo further explore the mechanisms through which DCR openings affect drug-related hospitalizations, I disaggregate the main outcome into five diagnostic\n    58\tsub-categories: (i) mental and behavioral disorders due to psychoactive substance use, (ii) viral hepatitis, (iii) HIV, (iv) poisoning by narcotics and psychodysleptics, and (v) fatal drug-related hospitalizations. Accidental poisoning is excluded due to the very small number of cases.\n    59\t\n    60\tFigure 7: Event study: Sub-outcome estimates,\n    61\twithin 5 km, \\pm 5-year window\n    62\t\n    63\tNote: The figure plots event-study coefficients from the doubly robust Callaway & Sant’Anna (2021) estimator for four diagnostic sub-categories of drug-related hospitalizations. Each panel uses a separate y -axis to accommodate differences in scale across outcomes. The sample includes all staggered-treated MedStat regions within 5 km of a\n    64\tDCR that opened between 1998 and 2022. The reference period is a universal base period. Regions near DCRs that opened before 1998 (always-treated) are excluded. 95% confidence intervals are based on standard errors clustered at the MedStat region level.\n    65\t\n    66\tTable 7: Overall ATT by Outcome Category and Buffer Zone\n    67\t\n    68\tOutcome | 1 km | 3 km | 5 km\n    69\t\n    70\tMental/Behavioral | -0.354 | -0.202 | -0.381*\n    71\t\n    72\t| (0.382) | (0.244) | (0.220)\n    73\t\n    74\tHepatitis | -0.270 | -0.413 | -0.406\n    75\t\n    76\t| (0.486) | (0.270) | (0.250)\n    77\t\n    78\tHIV | 0.063 | -0.239 | -0.340\n    79\t\n    80\t| (0.801) | (0.337) | (0.294)\n    81\t\n    82\tNarcotics | 1.186*** | 0.549*** | 0.390**\n    83\t\n    84\t| (0.288) | (0.187) | (0.165)\n    85\t\n    86\tDrug-related (fatal) | -1.207*** | -0.290 | -0.354**\n    87\t\n    88\t| (0.137) | (0.215) | (0.178)\n    89\t\n    90\tTreated regions | 7 | 35 | 50\n    91\t\n    92\tControl regions | 608 (never-treated) | |\n    93\t\n    94\tNote: Each cell reports the overall ATT from\n    95\tthe doubly robust Callaway & Sant’Anna (2021) estimator with\n    96\tnever-treated controls and a universal base period. Outcomes are\n    97\tlog hospitalization rates per 100,000 inhabitants. Sub-outcomes:\n    98\tmental and behavioral disorders (F11–F19), viral hepatitis (B15–B19),\n    99\tHIV disease (B20–B24), narcotics/psychodysleptics (T40). The 1 km\n   100\testimates are identified from a single treated region per cohort and\n   101\tshould be interpreted with caution. Regions near DCRs opened before\n   102\t1998 excluded. Standard errors clustered at the Medstat region level.\n   103\tSignificance: {}^{***}p<0.01 , {}^{**}p<0.05 , {}^{*}p<0.10 .\n   104\t\n   105\tFigure 7 and Table 7 repeat the estimation for the five diagnostic categories. From Table 7, the ATT estimates for hospitalizations due to mental and behavioral disorders are consistently negative and marginally significant at the 5 km buffer specification. Moreover, the estimates of hepatitis are also negative and of comparable magnitude but insignificant. However, the event-study path in Figure 7 shows a gradual decline that builds from year 3 onward, consistent with a slow-acting reduction in needle sharing. HIV estimates are insignificant and close to zero. Narcotics poisoning hospitalizations rise after DCR opening and remain positive across all buffers. Lastly, fatal drug-related hospitalizations move in the opposite direction, falling after opening the DCR. The opposing patterns in narcotics and fatal hospitalizations suggest that DCRs convert events that would otherwise be fatal into survivable hospital contacts.\n   106\t\n   107\t### 6.1 Mechanisms\n   108\t\n   109\tThe sub-outcome results point to two channels through which DCRs reduce drug-related hospitalizations. The first is overdose supervision. DCRs provide a medically supervised environment where staff can control the dose consumed, perform resuscitation, and call emergency services when an overdose occurs. When supervision prevents a death that would otherwise happen outside a hospital, the consequence is a simultaneous decline in fatal drug-related hospitalizations and a rise in non-fatal poisoning contacts, as events that would have ended in death instead end in a hospital visit. The results are consistent with this prediction. Drug-related fatal hospitalizations fall near DCRs, by approximately 30% at 5 km, while narcotics and psychodysleptics hospitalizations rise by 47% at 5 km. The opposing signs and spatial concentration of these two effects are consistent with a\n   110\tsubstitution from out-of-hospital mortality to in-hospital survival. Since I cannot observe overdose events that do not result in a hospital contact, this interpretation is suggestive rather than identified, but it is consistent with the clinical evidence on DCR-based overdose reversals\n   111\t(Kerr et al., 2017; Hedrich, 2004).\n   112\t\n   113\tThe second channel is harm reduction for blood-borne diseases. DCRs distribute sterile injection equipment, which reduces needle sharing and the transmission of viral hepatitis and HIV. Unlike overdose prevention, these effects are expected to emerge slowly, since disease progression and hospitalization take years to follow from initial infection. The event-study path for hepatitis is consistent with this lag. The estimates are close to zero in the first two post-treatment years and become progressively more negative from year 3 onward, reaching approximately 40% below baseline by year 5. HIV estimates are near zero through out the analysis, with wide confidence intervals. A longer panel and additional out-patient data would be needed to determine whether these trends continue and to get more precise estimates.\n   114\t\n   115\tA third potential channel is referral to treatment and social services. DCRs routinely connect users to addiction treatment, primary care, and housing support, and a sustained reduction in hospitalizations could partly reflect better long-term management of drug dependence. The regional hospitalization panel used in this paper does not contain information on treatment entry or service use, so I cannot separate this channel from the two described above. It remains a plausible additional mechanism that future work with information on treatment take up data could examine.\n   116\t\n   117\tTable A19: Robustness: Overall ATT Across Estimators — 5 km buffer, \\pm 5-year window\n   118\t\n   119\t| CS-DR | Sun & Abraham | Synthetic DiD | Stacked DiD\n   120\t\n   121\tATT | -0.431** | -0.438** | -0.362* | -0.149***\n   122\t\n   123\t| (0.218) | (0.208) | (0.205) | (0.038)\n   124\t\n   125\t% change | -35.0% | -35.5% | -30.3% | -13.9%\n   126\t\n   127\tTreated regions | 50 | | |\n   128\t\n   129\tControl regions | 608 | | |\n   130\t\n   131\tObservations | 17,250 | | |\n   132\t\n   133\tNote: All specifications use the 5 km buffer,\n   134\tnever-treated comparison regions (640 regions), and a \\pm 5-year window\n{\n  \"task_id\": \"F4-conf-01\",\n  \"required_files\": [\n    \"out/results.json\",\n    \"out/report.md\",\n    \"out/analysis.py\",\n    \"out/claims.json\"\n  ],\n  \"results_keys\": {\n    \"dcr_att_5km\": {\n      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Li; Shanna Pearson-Merkowitz; David Yokum\n     4\tBibliographic record: arXiv:2606.14621v1 [econ.GN], submitted 12 Jun 2026 (version 1, the only version at retrieval). Abstract page: https://arxiv.org/abs/2606.14621v1\n     5\tRetrieved: https://arxiv.org/html/2606.14621v1 on 2026-10-30T03:23:18Z (SHA-256 of the retrieved HTML: f98fb1b39edd2107051cddf1da34aa8cf19e74e04d60bf9305a88cf966417da1)\n     6\tLicense: CC BY 4.0 (license shown on the arXiv abstract page; https://creativecommons.org/licenses/by/4.0/). Reproduced in part with attribution.\n     7\tExtract contents: title, authors and abstract; Section 3 'Results' (Table 2 with caption, the results paragraphs, Section 3.1 'Cost-benefit analysis'); from Section 4.1, Table 3 with its caption and the paragraph that discusses it.\n     8\tOmitted: Sections 1-2, Table 1, Section 3.2 (heterogeneous effects), the rest of Section 4, the references and the appendix.\n     9\tRendering note: converted from the HTML rendering to plain text. Tables are rendered one row per line with \" | \" separating cells; in regression tables the line following a coefficient row holds the standard errors in parentheses, exactly as printed. Mathematical expressions appear as LaTeX source; non-breaking spaces were replaced by ordinary spaces. No numbers were altered.\n    10\tEditorial note: Tables 2 and 3 were re-set in a one-row-per-outcome layout because the HTML rendering split each outcome over three rows and detached the p-values from their columns; the p-values and confidence intervals are attached to the column they belong to in the original (first p-value = treatment-control difference, second = average treatment effect).\n    11\t================================================================\n    12\t\n    13\t# Evaluating the Impact of Rhode Island's Self-Sustaining Reemployment Services and Eligibility Assessment (RESEA) Program on Employment Outcomes\n    14\t\n    15\tHarrison H. Li (Harvey Mudd College), Shanna Pearson-Merkowitz (University of Maryland, College Park), David Yokum (University of North Carolina at Chapel Hill)\n    16\t\n    17\t###### Abstract\n    18\t\n    19\tProlonged unemployment carries serious economic, health, and wellbeing costs. With federal support, most U.S. states now operate a Reemployment Services and Eligibility Assessment (RESEA) program to help Unemployment Insurance (UI) claimants return to work faster. We report results from a large (N = 23,549) preregistered randomized controlled trial (RCT) evaluating Rhode Island’s RESEA program from February 2022 to September 2023. We estimate that selection into the program increased annualized wages by $1,153, increased reemployment by 1.5 percentage points, and reduced UI duration by nearly two weeks. The vast majority of these wage and reemployment effects appeared within two quarters of claimants’ first pay dates and persisted through at least the following year, and we estimate that each dollar spent on the program saved the state $2.64. Using causal forests, a machine learning technique for estimating heterogeneous treatment effects (HTE), we also conduct an exploratory analysis to investigate if there are differential effects of selection into the RESEA program. We find that all participants experienced positive wage benefits from RESEA selection, with particularly large effects for older and lower-income workers. Finally, we improve upon prior RESEA evaluations by explicitly controlling for the week of treatment assignment — a methodological refinement absent from several existing RCTs of job-training programs that is important to eliminate confounding bias. We also discuss ways to harvest precision gains from baseline covariate adjustment without introducing large-sample bias.\n    20\t\n    21\t## 3 Results\n    22\t\n    23\t[Table 1 (pre-treatment characteristics) omitted from this extract.]\n    24\t\n    25\t[Table 2 — re-set by the extract editor from the HTML rendering, in which each outcome occupied three table rows; all values are verbatim. Columns: Outcome | Treatment | Control | Treatment-control difference | Average treatment effect estimate]\n    26\t\n    27\tOutcome | Treatment | Control | Treatment-control difference | Average treatment effect estimate\n    28\tWage | $33,073 (SD: 36,749) | $33,043 (SD: 36,231) | $29; p=0.95; 95% CI: [-903, 962] | $1,153; p=0.022; 95% CI: [166, 2,141]\n    29\tReemployment | 75.2% | 75.0% | 0.26 pp; p=0.64; 95% CI: [-0.84, 1.37] | 1.47 pp; p=0.013; 95% CI: [0.32, 2.62]\n    30\tWeeks on UI | 15.2 weeks (SD: 9.3) | 17.3 weeks (SD: 9.4) | -2.10 weeks; p<0.001; 95% CI: [-2.34, -1.86] | -1.99 weeks; p<0.001; 95% CI: [-2.24, -1.75]\n    31\tShort-term wage | $23,776 (SD: 31,948) | $23,897 (SD: 32,293) | -$121; p=0.77; 95% CI: [-941, 700] | $1,195; p=0.007; 95% CI: [326, 2,064]\n    32\tShort-term reemployment | 61.4% | 61.6% | -0.18 pp; p=0.77; 95% CI: [-1.43, 1.06] | 1.66 pp; p=0.012; 95% CI: [0.37, 2.96]\n    33\tShort-term weeks on UI | 14.2 weeks (SD: 8.4) | 15.9 weeks (SD: 8.3) | -1.70 weeks; p<0.001; 95% CI: [-1.92, -1.49] | -1.77 weeks; p<0.001; 95% CI: [-1.99, -1.55]\n    34\t\n    35\tTable 2: The average values of our long-term and short-term outcomes for the aggregate treatment and control groups in the study cohort, along with information about the differences in these averages between the treatment and control groups and our average treatment effect estimates. For the average outcomes, standard deviations are given in parentheses for the quantitative outcomes (wage and weeks on UI). For the treatment-control differences and the average treatment effect estimates, we provide two-sided p -values and 95% confidence intervals based on Welch’s two-sample t -test and the methodology described in the main text, respectively. The two rightmost columns illustrate how direct comparisons of outcome averages between the aggregate treatment and control groups can lead to significantly biased causal conclusions.\n    36\t\n    37\tOverall, we find that RESEA selection led to a statistically significant improvement in all three of the preregistered outcomes. RESEA selection led to a wage outcome that was $1,153 higher annually (95% CI: [$166, $2,141]; p=0.022 ), a 3.5% increase relative to the mean wage outcome of $33,043 in the control group (Table 2). There was also an estimated 1.47 percentage point increase in reemployment (95% CI: [0.32, 2.62]; p=0.013 ) and a 1.99 week reduction in the number of weeks on UI (95% CI: [1.75 weeks, 2.24 weeks]; p<0.001 ) due to RESEA selection. The latter is an 11.5% decrease compared to the mean control group weeks on UI outcome of 17.3 weeks. All three of these results remain statistically significant after correcting for multiple testing using Holm’s method to keep the familywise error rate below 5 percent.\n    38\t\n    39\tThe results for the short-term outcomes are substantively similar to those for the longer-term outcomes (Table 2). We estimate RESEA selection leads to a short-term wage outcome that is $1,195 higher (95% CI: [$326, $2,064]; p = 0.007) on an annualized basis and a 1.66 percentage point higher rate of short-term reemployment (95% CI: [0.37, 2.96]; p = 0.012). Given that the magnitudes of these causal estimates are very similar to those for the corresponding long-term outcomes, this suggests that the full impact of RESEA selection on wages and reemployment appears quickly by the second quarter after randomization, and persists for at least five quarters. We also find that those selected for RESEA spend 1.77 fewer weeks on UI in the benefit year of their claim (95% CI: [1.55 weeks, 1.99 weeks]; p ¡ 0.001). As this is a bit smaller than the effect size of 1.99 weeks estimated for the long term weeks on UI outcome, which includes an additional 26 weeks beyond the benefit year, we see some directional evidence that RESEA selection may also reduce the time spent on UI in the following year, suggesting that it may be helping claimants find employment that lasts longer, thereby reducing repetitive use of UI benefits.\n    40\t\n    41\tAs noted earlier, wages and reemployment are artificially zero-inflated due to missing out-of-state wage data. The Rhode Island job market is tightly integrated with those of the neighboring states of Massachusetts and Connecticut; the 2016-2020 5-Year American Community Survey found that nearly 16 percent of Rhode Island workers 16 years and older commuted out of state, mainly to Massachusetts. We note, however, that this missingness should not affect the directional validity of our causal analysis. This is because randomization ensures that this missingness is balanced between the treatment and control groups (within each block), unless RESEA selection itself differentially helps or hinders finding employment out of state or self-employment. To investigate this, we examined the presence of individuals who failed to exhaust their UI benefits in the benefit year of their claim while having $0 in reported RI wages for all of the five complete quarters after randomization. There were 3,325 claimants falling into this group, including 1,618 individuals (13.7%) in the aggregate control group and 1,707 individuals (14.6%) in the aggregate treatment group. After controlling for randomization week, this difference in proportions was not significant ( p = 0.55). The lack of statistical significance on this test with our large sample size along with the fact that the Rhode Island job database focuses overwhelmingly on jobs offered by Rhode Island employers suggests that RESEA selection does not differentially assist in finding out-of-state employment or self-employment.\n    42\t\n    43\t### 3.1 Cost-benefit analysis\n    44\t\n    45\tWe find that the decrease in time spent drawing UI benefits due to RESEA selection corresponds to a considerable cost savings to the state from paying out a smaller amount of UI benefits. We estimate that the average individual selected for RESEA during the study period drew $676 less (95% CI: [$563, $789], p<0.001 ) in UI benefits during the same benefit year of their claim than they would have if they were not selected for RESEA. This is a 9.9 percent reduction relative to the control group average of $6,839 in UI benefits drawn during the benefit year of the claim. Multiplying the estimate by the total number of individuals selected for RESEA in the study ( n=11,700 ) suggests that as a result of the program, the state paid out about $7.9 million fewer in UI benefits to claimants randomized during the study period over the same benefit year as their UI claims. This is much higher than the total expenditures of $2.99 million for the RESEA program over the study period, representing $2.64 in savings for the state budget for every $1 spent on the program.\n    46\t\n    47\t### 4.1 Methodological lessons (excerpt)\n    48\t\n    49\t[Table 3 — re-set by the extract editor from the HTML rendering; all values are verbatim. Columns: Outcome | Difference in means | Covariate adjusted | Fixed effects regression]\n    50\t\n    51\tOutcome | Difference in means | Covariate adjusted | Fixed effects regression\n    52\tWage | $1,153; p=0.022; 95% CI: [166, 2,141] | $1,137; p=0.014; 95% CI: [228, 2,047] | $1,100; p=0.027; 95% CI: [127, 2,073]\n    53\tReemployment | 1.47 pp; p=0.013; 95% CI: [0.32, 2.62] | 1.48 pp; p=0.012; 95% CI: [0.32, 2.63] | 1.49 pp; p=0.011; 95% CI: [0.34, 2.64]\n    54\tWeeks on UI | -1.99 wks; p<0.001; 95% CI: [-2.24, -1.75] | -1.93 wks; p<0.001; 95% CI: [-2.18, -1.68] | -1.97 wks; p<0.001; 95% CI: [-2.22, -1.72]\n    55\t\n    56\tTable 3: A comparison of the ITT estimates, two-sided p -values, and 95% confidence intervals for our primary outcomes from three different methodologies: the preregistered analysis based on eq. (1), the covariate adjusted analysis based on eq. (2), and the fixed effects regression in eq. (3). The leftmost column based on the preregistered analysis duplicates the rightmost column in Table 2 for ease of comparison.\n    57\t\n    58\tIf we directly compared treatment group and control group outcome averages without accounting for the week of randomization, the results would suggest RESEA selection had no detectable average effect on the wage and reemployment outcomes (column 4, Table 2). However, such an analysis is potentially biased under the experimental design because it does not respect the blocked randomization, which makes the week of randomization a known confounder. For example, weeks with a larger number of UI applicants, which had a higher proportion of control group individuals, tended to have lower-earning applicants (Fig. 7), thereby inflating the average wage outcome in the control group relative to the treatment group. This is critical because out of the thirteen publically available studies of RESEA-related employment assistance programs rated by CLEAR as having high causal evidence, six used block randomization like we do, yet only two of these (Black, Smith, Berger, & Noel., 2002; Klerman et al., 2019) properly adjusted for the block. Three others among these six studies used regression or matching methods in an attempt to control for observed demographic variables but not the block (Poe-Yamagata et al., 2011; Michaelides et al., 2012; Manoli et al., 2018), while the remaining study (Michaelides & Mueser, 2018) fit a linear regression model with the block indicators as fixed effects, as did two other more recent evaluations in Nevada (Michaelides & Mian, 2021; Brigandi et al., 2024) . 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-p out/state && cd out/state && cat > contract.md <<'EOF'\n# Contract — F4-conf-01\n**Question.** Extract, exactly as printed, specified treatment-effect estimates and their uncertainty from two arXiv extracts (Armendariz 2026 DCR staggered DiD, Tables 6/7; Li et al. 2026 RESEA RCT, Table 2), attach locators, derive (a) a 95% CI for the Table 6 5 km ATT from its printed SE and (b) an SE for the Table 2 wage ATE from its printed 95% CI, with a stated convention, and recognise unreported quantities (e.g. exact p-value of HIV 5 km ATT; SE of wage ATE).\n**Estimands.** The printed numbers themselves; derived quantities assume approximate normality of the estimator (Wald-type CI, critical value 1.96) and, for the wage SE, that the printed CI is symmetric Wald-type.\n**Outputs.** out/results.json (keys listed in TASK.md incl. locators, derivation_notes, task_id, feasibility, feasibility_reason, data_issues, n_input_rows, n_analysed, random_seed=null), out/report.md (6 sections), out/analysis.py (regenerates results from data/ and prints matched lines), out/claims.json, plus state files and out/exclusions.csv.\n**Information rules.** Only the two extracts are evidence; no memory; values as printed from the table the key names (Table 6 not A19 for the 5 km ATT SE); null + \"not reported\" locator for unprinted quantities.\n**Invalidity.** Using values from wrong table, rounding/rescaling printed values, fabricating p-values, unstated derivation conventions.\nEOF\ncat > phases.json <<'EOF'\n{\"P0\":{\"status\":\"passed\",\"checks\":[\"TASK.md read fully\",\"contract.md written\"],\"notes\":\"\"},\n\"P1\":{\"status\":\"pending\",\"checks\":[],\"notes\":\"\"},\"P2\":{\"status\":\"pending\",\"checks\":[],\"notes\":\"\"},\n\"P3\":{\"status\":\"pending\",\"checks\":[],\"notes\":\"\"},\"P4\":{\"status\":\"pending\",\"checks\":[],\"notes\":\"\"},\"P5\":{\"status\":\"pending\",\"checks\":[],\"notes\":\"\"}}\nEOF\necho \"- 2026-10-30 P0 passed: contract written.\" > 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-p out/state && cd out/state && cat > contract.md <<'EOF'\n# Contract — F4-conf-01\n**Question.** Extract, exactly as printed, specified treatment-effect estimates and their uncertainty from two arXiv extracts (Armendariz 2026 DCR staggered DiD, Tables 6/7; Li et al. 2026 RESEA RCT, Table 2), attach locators, derive (a) a 95% CI for the Table 6 5 km ATT from its printed SE and (b) an SE for the Table 2 wage ATE from its printed 95% CI, with a stated convention, and recognise unreported quantities (e.g. exact p-value of HIV 5 km ATT; SE of wage ATE).\n**Estimands.** The printed numbers themselves; derived quantities assume approximate normality of the estimator (Wald-type CI, critical value 1.96) and, for the wage SE, that the printed CI is symmetric Wald-type.\n**Outputs.** out/results.json (keys listed in TASK.md incl. locators, derivation_notes, task_id, feasibility, feasibility_reason, data_issues, n_input_rows, n_analysed, random_seed=null), out/report.md (6 sections), out/analysis.py (regenerates results from data/ and prints matched lines), out/claims.json, plus state files and out/exclusions.csv.\n**Information rules.** Only the two extracts are evidence; no memory; values as printed from the table the key names (Table 6 not A19 for the 5 km ATT SE); null + \"not reported\" locator for unprinted quantities.\n**Invalidity.** Using values from wrong table, rounding/rescaling printed values, fabricating p-values, unstated derivation conventions.\nEOF\ncat > phases.json <<'EOF'\n{\"P0\":{\"status\":\"passed\",\"checks\":[\"TASK.md read fully\",\"contract.md written\"],\"notes\":\"\"},\n\"P1\":{\"status\":\"pending\",\"checks\":[],\"notes\":\"\"},\"P2\":{\"status\":\"pending\",\"checks\":[],\"notes\":\"\"},\n\"P3\":{\"status\":\"pending\",\"checks\":[],\"notes\":\"\"},\"P4\":{\"status\":\"pending\",\"checks\":[],\"notes\":\"\"},\"P5\":{\"status\":\"pending\",\"checks\":[],\"notes\":\"\"}}\nEOF\necho \"- 2026-10-30 P0 passed: contract written.\" > 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> out/analysis.py <<'EOF'\n\"\"\"F4-conf-01: extract printed values from two arXiv extracts; derive two quantities.\nReads only data/; writes out/results.json deterministically. No randomness.\"\"\"\nimport json, re, os, hashlib\n\nROOT = os.path.dirname(os.path.dirname(os.path.abspath(__file__)))\nD = os.path.join(ROOT, \"data\")\nDCR_ID, RI_ID = \"arxiv-2609.27732v1\", \"arxiv-2606.14621v1\"\nZ = 1.96  # normal critical value for two-sided 95% intervals\n\n\ndef read(rel):\n    with open(os.path.join(D, rel), encoding=\"utf-8\") as f:\n        return f.read()\n\n\ndef wc_l(text):\n    return text.count(\"\\n\")\n\n\nreadme = read(\"README.md\")\ndcr = read(f\"sources/{DCR_ID}.txt\")\nri = read(f\"sources/{RI_ID}.txt\")\nn_input_rows = {\"README.md\": wc_l(readme), f\"sources/{DCR_ID}.txt\": wc_l(dcr),\n                f\"sources/{RI_ID}.txt\": wc_l(ri)}\n\n# verify SHA-256 against README provenance table\nfor name, txt in [(DCR_ID, dcr), (RI_ID, ri)]:\n    h = hashlib.sha256(txt.encode(\"utf-8\")).hexdigest()\n    assert h in readme, f\"SHA mismatch for {name}\"\n    print(f\"[sha256 ok] {name} {h}\")\n\ndcr_lines = [l for l in dcr.split(\"\\n\") if l.strip()]\n\n\ndef table_block(lines, start_pat, end_pat):\n    i = next(k for k, l in enumerate(lines) if l.startswith(start_pat))\n    j = next(k for k, l in enumerate(lines) if k > i and l.startswith(end_pat))\n    return lines[i:j]\n\n\ndef cells(line):\n    return [c.strip() for c in line.split(\"|\")]\n\n\ndef num(s):\n    \"\"\"Parse a printed number: strip stars, parentheses, $, commas, %; keep sign.\"\"\"\n    t = s.replace(\"$\", \"\").replace(\",\", \"\").replace(\"*\", \"\").strip(\"() %\")\n    return float(t)\n\n\ndef coef_and_se(block, row_label, col_label, header_idx=0):\n    header = cells(block[header_idx])\n    col = header.index(col_label)\n    k = next(k for k, l in enumerate(block) if cells(l)[0] == row_label)\n    row, se_row = cells(block[k]), cells(block[k + 1])\n    assert se_row[0] == \"\" and se_row[col].startswith(\"(\"), se_row\n    print(f\"[match] row: {block[k]!r}\\n        se : {block[k+1]!r}  -> col {col_label!r}: {row[col]} / {se_row[col]}\")\n    return row[col], se_row[col]\n\n\n# ---- DCR Table 6\nt6 = table_block(dcr_lines, \"Table 6:\", \"Note:\")[1:]\natt5_s, se5_s = coef_and_se(t6, \"ATT\", \"5 km\")\natt3_s, se3_s = coef_and_se(t6, \"ATT\", \"3 km\")\nhdr6 = cells(t6[0])\nobs_line = next(l for l in t6 if cells(l)[0] == \"Observations\")\nprint(f\"[match] {obs_line!r}\")\nobs5 = int(cells(obs_line)[hdr6.index(\"5 km\")].replace(\",\", \"\"))\n\n# ---- DCR Table 7\nt7 = table_block(dcr_lines, \"Table 7:\", \"Note:\")[1:]\nnarc_s, narc_se_s = coef_and_se(t7, \"Narcotics\", \"3 km\")\nfatal_s, fatal_se_s = coef_and_se(t7, \"Drug-related (fatal)\", \"1 km\")\nhiv_s, hiv_se_s = coef_and_se(t7, \"HIV\", \"5 km\")\nhiv_sig10 = \"*\" in hiv_s  # any star = p<0.10 per table note\n# exact p-value: does the extract print one for Table 7 cells? check for 'p=' / 'p-value' in Table 7 block\nt7_has_p = any(re.search(r\"p\\s*=\\s*0?\\.\\d\", l) for l in t7)\nhiv_p = None\nprint(f\"[check] Table 7 prints exact p-values: {t7_has_p}\")\n\n# ---- Table A19 (only used for consistency check)\nta19 = table_block(dcr_lines, \"Table A19:\", \"Note:\")[1:]\na19_att, a19_se = coef_and_se(ta19, \"ATT\", \"CS-DR\")\n\n# ---- RI Table 2\nri_lines = ri.split(\"\\n\")\nhdr_idx = next(k for k, l in enumerate(ri_lines) if l.startswith(\"Outcome | Treatment | Control\"))\nhdr2 = cells(ri_lines[hdr_idx])\nt2 = {}\nfor l in ri_lines[hdr_idx + 1:]:\n    if not l.strip():\n        break\n    c = cells(l)\n    t2[c[0]] = dict(zip(hdr2, c))\nATE = \"Average treatment effect estimate\"\n\n\ndef parse_effect(cell):\n    m = re.match(r\"^(-?\\$?-?[\\d,]+(?:\\.\\d+)?)\\s*(?:pp|weeks|wks)?;\\s*p\\s*([=<])\\s*([\\d.]+);\\s*95% CI:\\s*\\[([^,\\]]+(?:,\\d{3})?),\\s*([^\\]]+)\\]$\", cell)\n    assert m, cell\n    return m.group(1), m.group(2) + m.group(3), m.group(4).strip(), m.group(5).strip()\n\n\nwage_cell = t2[\"Wage\"][ATE]\nreemp_cell = t2[\"Reemployment\"][ATE]\nweeks_cell = t2[\"Weeks on UI\"][ATE]\nctrl_cell = t2[\"Wage\"][\"Control\"]\nfor lab in [\"Wage\", \"Reemployment\", \"Weeks on UI\"]:\n    print(f\"[match] Table 2 {lab!r} ATE cell: {t2[lab][ATE]!r}\")\nprint(f\"[match] Table 2 'Wage' Control cell: {ctrl_cell!r}\")\nw_pt, w_p, w_lo, w_hi = parse_effect(wage_cell)\nr_pt, r_p, r_lo, r_hi = parse_effect(reemp_cell)\nu_pt, u_p, u_lo, u_hi = parse_effect(weeks_cell)\nctrl_mean = num(re.match(r\"^(\\$[\\d,]+)\", ctrl_cell).group(1))\nwage_se_reported = bool(re.search(r\"\\bSE\\b|standard error|\\(\\s*[\\d,.]+\\s*\\)\", wage_cell))\n\n# ---- derived\natt5, se5 = num(att5_s), num(se5_s)\nci_lo = round(att5 - Z * se5, 4)\nci_hi = round(att5 + Z * se5, 4)\nwlo, whi = num(w_lo), num(w_hi)\nwage_se = round((whi - wlo) / (2 * Z), 2)\n\nres = {\n    \"task_id\": \"F4-conf-01\",\n    \"dcr_att_5km\": att5, \"dcr_att_5km_se\": se5,\n    \"dcr_att_3km\": num(att3_s), \"dcr_att_3km_se\": num(se3_s),\n    \"dcr_obs_5km\": obs5,\n    \"dcr_narcotics_att_3km\": num(narc_s), \"dcr_narcotics_att_3km_se\": num(narc_se_s),\n    \"dcr_fatal_att_1km\": num(fatal_s), \"dcr_fatal_att_1km_se\": num(fatal_se_s),\n    \"dcr_hiv_att_5km\": num(hiv_s), \"dcr_hiv_att_5km_se\": num(hiv_se_s),\n    \"dcr_hiv_att_5km_p_value\": hiv_p,\n    \"dcr_hiv_att_5km_significant_10pct\": hiv_sig10,\n    \"dcr_att_5km_ci_low\": ci_lo, \"dcr_att_5km_ci_high\": ci_hi,\n    \"ri_wage_ate\": num(w_pt), \"ri_wage_ate_ci_low\": wlo, \"ri_wage_ate_ci_high\": whi,\n    \"ri_wage_ate_se_reported\": wage_se_reported,\n    \"ri_wage_ate_se_derived\": wage_se,\n    \"ri_control_mean_wage\": ctrl_mean,\n    \"ri_reemployment_ate_pp\": num(r_pt),\n    \"ri_weeks_ui_ate\": num(u_pt), \"ri_weeks_ui_ate_ci_low\": num(u_lo), \"ri_weeks_ui_ate_ci_high\": num(u_hi),\n}\n\nL6 = f\"{DCR_ID} | Table 6\"\nL7 = f\"{DCR_ID} | Table 7\"\nL2 = f\"{RI_ID} | Table 2\"\nres[\"locators\"] = {\n    \"dcr_att_5km\": f'{L6} | row \"ATT\" | column \"5 km\"',\n    \"dcr_att_5km_se\": f'{L6} | row \"ATT\" (standard-error line in parentheses) | column \"5 km\"',\n    \"dcr_att_3km\": f'{L6} | row \"ATT\" | column \"3 km\"',\n    \"dcr_att_3km_se\": f'{L6} | row \"ATT\" (standard-error line in parentheses) | column \"3 km\"',\n    \"dcr_obs_5km\": f'{L6} | row \"Observations\" | column \"5 km\"',\n    \"dcr_narcotics_att_3km\": f'{L7} | row \"Narcotics\" | column \"3 km\"',\n    \"dcr_narcotics_att_3km_se\": f'{L7} | row \"Narcotics\" (standard-error line in parentheses) | column \"3 km\"',\n    \"dcr_fatal_att_1km\": f'{L7} | row \"Drug-related (fatal)\" | column \"1 km\"',\n    \"dcr_fatal_att_1km_se\": f'{L7} | row \"Drug-related (fatal)\" (standard-error line in parentheses) | column \"1 km\"',\n    \"dcr_hiv_att_5km\": f'{L7} | row \"HIV\" | column \"5 km\"',\n    \"dcr_hiv_att_5km_se\": f'{L7} | row \"HIV\" (standard-error line in parentheses) | column \"5 km\"',\n    \"dcr_hiv_att_5km_p_value\": f\"not reported in {DCR_ID} (Table 7 prints only significance stars, no exact p-values; an exact p-value could only be derived under an additional distributional assumption)\",\n    \"dcr_att_5km_ci_low\": f'{L6} | row \"ATT\" | column \"5 km\" | derived from printed ATT and SE (ATT - 1.96*SE)',\n    \"dcr_att_5km_ci_high\": f'{L6} | row \"ATT\" | column \"5 km\" | derived from printed ATT and SE (ATT + 1.96*SE)',\n    \"ri_wage_ate\": f'{L2} | row \"Wage\" | column \"Average treatment effect estimate\" (point estimate)',\n    \"ri_wage_ate_ci_low\": f'{L2} | row \"Wage\" | column \"Average treatment effect estimate\" (95% CI lower bound)',\n    \"ri_wage_ate_ci_high\": f'{L2} | row \"Wage\" | column \"Average treatment effect estimate\" (95% CI upper bound)',\n    \"ri_wage_ate_se_derived\": f'{L2} | row \"Wage\" | column \"Average treatment effect estimate\" | derived from printed 95% CI ((high - low)/(2*1.96)); SE not reported in {RI_ID}',\n    \"ri_control_mean_wage\": f'{L2} | row \"Wage\" | column \"Control\" (mean)',\n    \"ri_reemployment_ate_pp\": f'{L2} | row \"Reemployment\" | column \"Average treatment effect estimate\" (point estimate)',\n    \"ri_weeks_ui_ate\": f'{L2} | row \"Weeks on UI\" | column \"Average treatment effect estimate\" (point estimate)',\n    \"ri_weeks_ui_ate_ci_low\": f'{L2} | row \"Weeks on UI\" | column \"Average treatment effect estimate\" (95% CI lower bound)',\n    \"ri_weeks_ui_ate_ci_high\": f'{L2} | row \"Weeks on UI\" | column \"Average treatment effect estimate\" (95% CI upper bound)',\n}\nres[\"derivation_notes\"] = {\n    \"dcr_att_5km_ci\": \"Normal (Wald) approximation with critical value 1.96: ATT +/- 1.96*SE using Table 6 printed ATT -0.431 and clustered SE 0.210; bounds rounded to 4 decimals. Assumes asymptotic normality of the Callaway-Sant'Anna estimator; the paper's own (event-study) intervals are also described as 95% CIs from clustered SEs but no CI for the overall ATT is printed.\",\n    \"ri_wage_ate_se\": \"SE = (upper - lower)/(2*1.96) from the Table 2 printed 95% CI [166, 2,141]; rounded to 2 decimals. Assumes the CI is a symmetric normal-approximation (Wald) interval; the printed CI is very nearly symmetric about $1,153 (987 below, 988 above). With n = 23,549 a t critical value is indistinguishable from 1.96.\",\n}\n# consistency checks -> data issues\nissues = []\nif (a19_att.replace(\"*\", \"\"), a19_se) == (att5_s.replace(\"*\", \"\"), se5_s):\n    pass\nelse:\n    issues.append({\"issue\": f\"Internal inconsistency in {DCR_ID}: the 5 km CS-DR ATT {att5_s} has SE {se5_s} in Table 6 but SE {a19_se} in Appendix Table A19 (same estimator, buffer, window and N=17,250).\",\n                   \"handling\": \"Used Table 6 as required by the key definitions; derived CI uses the Table 6 SE 0.210. Flagged; not resolved (source does not explain).\"})\nif \"640 regions\" in dcr:\n    issues.append({\"issue\": f\"Internal inconsistency in {DCR_ID}: Tables 6/7 and the Figure 6 note report 608 never-treated control regions, but the Table A19 note says 640 never-treated comparison regions.\",\n                   \"handling\": \"No requested key depends on it; flagged only.\"})\nif \"fall by 35%\" in dcr and \"approximately 30% at 5 km\" in dcr:\n    issues.append({\"issue\": f\"Text inconsistency in {DCR_ID}: abstract says fatal hospitalisations fall by 35%, Section 6.1 says ~30% at 5 km (Table 7 fatal 5 km ATT -0.354 implies exp(-0.354)-1 = -29.8%). Section 6 text also calls HIV estimates 'close to zero' although the 5 km point estimate is -0.340.\",\n                   \"handling\": \"Narrative only; all keys taken from the tables. Flagged.\"})\nif \"[1.75 weeks, 2.24 weeks]\" in ri:\n    issues.append({\"issue\": f\"Sign convention differs in {RI_ID}: text reports the weeks-on-UI CI as magnitudes [1.75, 2.24] of a reduction, Table 2 prints the ATE -1.99 with CI [-2.24, -1.75].\",\n                   \"handling\": \"Used Table 2 signed values as printed (lower bound first).\"})\nif \"p ¡ 0.001\" in ri:\n    issues.append({\"issue\": f\"Rendering defect in {RI_ID} text: 'p ¡ 0.001' (should read p < 0.001).\",\n                   \"handling\": \"Does not affect any key; flagged.\"})\nissues.append({\"issue\": f\"{RI_ID} Table 2 was re-set by the extract editor (one row per outcome; p-values reattached to columns) per the extract header.\",\n               \"handling\": \"Accepted the editor's layout; Table 3 'Difference in means' column duplicates Table 2 ATE values exactly (checked), supporting correct reattachment.\"})\nt3w = next(l for l in ri_lines if l.startswith(\"Wage | $1,153\"))\nassert cells(t3w)[1] == wage_cell, \"Table 3 / Table 2 wage mismatch\"\nres[\"data_issues\"] = issues\nres[\"feasibility\"] = \"complete\"\nres[\"feasibility_reason\"] = None\nres[\"n_input_rows\"] = n_input_rows\nvalue_keys = [k for k in res if k.startswith((\"dcr_\", \"ri_\"))]\nres[\"n_analysed\"] = len(value_keys)\nres[\"random_seed\"] = None\n\nos.makedirs(os.path.join(ROOT, \"out\"), exist_ok=True)\nwith open(os.path.join(ROOT, \"out\", \"results.json\"), \"w\") as f:\n    json.dump(res, f, indent=2, ensure_ascii=False)\nprint(json.dumps({k: res[k] for k in value_keys}, indent=1))\nEOF\n~/wp29/venv/bin/python out/analysis.py","description":"Write and run analysis script"},"caller":{"type":"direct"}}],"container":null,"stop_reason":"tool_use","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":1813,"cache_read_input_tokens":21557,"output_tokens":5962,"output_tokens_details":{"thinking_tokens":0},"server_tool_use":{"web_search_requests":0,"web_fetch_requests":0},"service_tier":"standard","cache_creation":{"ephemeral_1h_input_tokens":0,"ephemeral_5m_input_tokens":1813},"inference_geo":"global","iterations":[],"speed":"standard","fallback_credit":null},"input_transformations":[],"diagnostics":null,"context_management":null},"apiBlockIndex":1,"requestId":"req_011CgXsCERyQeYdKRw9Ns9yr","requestRef":"055d00a5-85a4-4493-a754-67cc1378bbef","requestedModel":"claude-opus-5-5","type":"assistant","uuid":"837e2323-b272-4b1b-865e-0d3f4a0b4570","timestamp":"2026-10-30T05:26:41.227Z","effort":"medium","perTurnEffort":"medium","userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0288/out/state","sessionId":"6a59845e-54b1-4582-86f5-752e1f4df7e4","version":"2.1.314","gitBranch":"HEAD"}35{"parentUuid":"837e2323-b272-4b1b-865e-0d3f4a0b4570","isSidechain":false,"promptId":"214a8ece-2bbc-459c-aa1a-66062e2a4650","type":"user","message":{"role":"user","content":[{"type":"tool_result","content":"Exit code 2\n(eval):1: no such file or directory: out/analysis.py\n/opt/homebrew/Cellar/python@3.14/3.14.4/Frameworks/Python.framework/Versions/3.14/Resources/Python.app/Contents/MacOS/Python: can't open file '/Users/simon-pierreboucher/wp29/runs/RC0288/out/state/out/analysis.py': [Errno 2] No such file or directory","is_error":true,"tool_use_id":"toolu_01GeQGXnbbih6pDa2497hKMQ"}]},"uuid":"6ba13ef7-c036-4134-814f-f98c70ad1deb","timestamp":"2026-10-30T05:26:41.268Z","toolUseResult":"Error: Exit code 2\n(eval):1: no such file or directory: out/analysis.py\n/opt/homebrew/Cellar/python@3.14/3.14.4/Frameworks/Python.framework/Versions/3.14/Resources/Python.app/Contents/MacOS/Python: can't open file '/Users/simon-pierreboucher/wp29/runs/RC0288/out/state/out/analysis.py': [Errno 2] No such file or directory","sandboxed":false,"sourceToolAssistantUUID":"837e2323-b272-4b1b-865e-0d3f4a0b4570","permissionDecision":{"decision":"accept","source":"config","reasonType":"rule","ruleSource":"cliArg"},"userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0288/out/state","sessionId":"6a59845e-54b1-4582-86f5-752e1f4df7e4","version":"2.1.314","gitBranch":"HEAD"}36{"type":"api-request-blob","sessionId":"6a59845e-54b1-4582-86f5-752e1f4df7e4","hash":"9ae32cc0db47853ed7ae0dfa7271215d269b51c0846274f497a450016cd48561","message":{"role":"assistant","content":[{"type":"text","text":"Now the analysis script."},{"type":"tool_use","id":"toolu_01GeQGXnbbih6pDa2497hKMQ","name":"Bash","input":{"command":"cat > out/analysis.py <<'EOF'\n\"\"\"F4-conf-01: extract printed values from two arXiv extracts; derive two quantities.\nReads only data/; writes out/results.json deterministically. No randomness.\"\"\"\nimport json, re, os, hashlib\n\nROOT = os.path.dirname(os.path.dirname(os.path.abspath(__file__)))\nD = os.path.join(ROOT, \"data\")\nDCR_ID, RI_ID = \"arxiv-2609.27732v1\", \"arxiv-2606.14621v1\"\nZ = 1.96  # normal critical value for two-sided 95% intervals\n\n\ndef read(rel):\n    with open(os.path.join(D, rel), encoding=\"utf-8\") as f:\n        return f.read()\n\n\ndef wc_l(text):\n    return text.count(\"\\n\")\n\n\nreadme = read(\"README.md\")\ndcr = read(f\"sources/{DCR_ID}.txt\")\nri = read(f\"sources/{RI_ID}.txt\")\nn_input_rows = {\"README.md\": wc_l(readme), f\"sources/{DCR_ID}.txt\": wc_l(dcr),\n                f\"sources/{RI_ID}.txt\": wc_l(ri)}\n\n# verify SHA-256 against README provenance table\nfor name, txt in [(DCR_ID, dcr), (RI_ID, ri)]:\n    h = hashlib.sha256(txt.encode(\"utf-8\")).hexdigest()\n    assert h in readme, f\"SHA mismatch for {name}\"\n    print(f\"[sha256 ok] {name} {h}\")\n\ndcr_lines = [l for l in dcr.split(\"\\n\") if l.strip()]\n\n\ndef table_block(lines, start_pat, end_pat):\n    i = next(k for k, l in enumerate(lines) if l.startswith(start_pat))\n    j = next(k for k, l in enumerate(lines) if k > i and l.startswith(end_pat))\n    return lines[i:j]\n\n\ndef cells(line):\n    return [c.strip() for c in line.split(\"|\")]\n\n\ndef num(s):\n    \"\"\"Parse a printed number: strip stars, parentheses, $, commas, %; keep sign.\"\"\"\n    t = s.replace(\"$\", \"\").replace(\",\", \"\").replace(\"*\", \"\").strip(\"() %\")\n    return float(t)\n\n\ndef coef_and_se(block, row_label, col_label, header_idx=0):\n    header = cells(block[header_idx])\n    col = header.index(col_label)\n    k = next(k for k, l in enumerate(block) if cells(l)[0] == row_label)\n    row, se_row = cells(block[k]), cells(block[k + 1])\n    assert se_row[0] == \"\" and se_row[col].startswith(\"(\"), se_row\n    print(f\"[match] row: {block[k]!r}\\n        se : {block[k+1]!r}  -> col {col_label!r}: {row[col]} / {se_row[col]}\")\n    return row[col], se_row[col]\n\n\n# ---- DCR Table 6\nt6 = table_block(dcr_lines, \"Table 6:\", \"Note:\")[1:]\natt5_s, se5_s = coef_and_se(t6, \"ATT\", \"5 km\")\natt3_s, se3_s = coef_and_se(t6, \"ATT\", \"3 km\")\nhdr6 = cells(t6[0])\nobs_line = next(l for l in t6 if cells(l)[0] == \"Observations\")\nprint(f\"[match] {obs_line!r}\")\nobs5 = int(cells(obs_line)[hdr6.index(\"5 km\")].replace(\",\", \"\"))\n\n# ---- DCR Table 7\nt7 = table_block(dcr_lines, \"Table 7:\", \"Note:\")[1:]\nnarc_s, narc_se_s = coef_and_se(t7, \"Narcotics\", \"3 km\")\nfatal_s, fatal_se_s = coef_and_se(t7, \"Drug-related (fatal)\", \"1 km\")\nhiv_s, hiv_se_s = coef_and_se(t7, \"HIV\", \"5 km\")\nhiv_sig10 = \"*\" in hiv_s  # any star = p<0.10 per table note\n# exact p-value: does the extract print one for Table 7 cells? check for 'p=' / 'p-value' in Table 7 block\nt7_has_p = any(re.search(r\"p\\s*=\\s*0?\\.\\d\", l) for l in t7)\nhiv_p = None\nprint(f\"[check] Table 7 prints exact p-values: {t7_has_p}\")\n\n# ---- Table A19 (only used for consistency check)\nta19 = table_block(dcr_lines, \"Table A19:\", \"Note:\")[1:]\na19_att, a19_se = coef_and_se(ta19, \"ATT\", \"CS-DR\")\n\n# ---- RI Table 2\nri_lines = ri.split(\"\\n\")\nhdr_idx = next(k for k, l in enumerate(ri_lines) if l.startswith(\"Outcome | Treatment | Control\"))\nhdr2 = cells(ri_lines[hdr_idx])\nt2 = {}\nfor l in ri_lines[hdr_idx + 1:]:\n    if not l.strip():\n        break\n    c = cells(l)\n    t2[c[0]] = dict(zip(hdr2, c))\nATE = \"Average treatment effect estimate\"\n\n\ndef parse_effect(cell):\n    m = re.match(r\"^(-?\\$?-?[\\d,]+(?:\\.\\d+)?)\\s*(?:pp|weeks|wks)?;\\s*p\\s*([=<])\\s*([\\d.]+);\\s*95% CI:\\s*\\[([^,\\]]+(?:,\\d{3})?),\\s*([^\\]]+)\\]$\", cell)\n    assert m, cell\n    return m.group(1), m.group(2) + m.group(3), m.group(4).strip(), m.group(5).strip()\n\n\nwage_cell = t2[\"Wage\"][ATE]\nreemp_cell = t2[\"Reemployment\"][ATE]\nweeks_cell = t2[\"Weeks on UI\"][ATE]\nctrl_cell = t2[\"Wage\"][\"Control\"]\nfor lab in [\"Wage\", \"Reemployment\", \"Weeks on UI\"]:\n    print(f\"[match] Table 2 {lab!r} ATE cell: {t2[lab][ATE]!r}\")\nprint(f\"[match] Table 2 'Wage' Control cell: {ctrl_cell!r}\")\nw_pt, w_p, w_lo, w_hi = parse_effect(wage_cell)\nr_pt, r_p, r_lo, r_hi = parse_effect(reemp_cell)\nu_pt, u_p, u_lo, u_hi = parse_effect(weeks_cell)\nctrl_mean = num(re.match(r\"^(\\$[\\d,]+)\", ctrl_cell).group(1))\nwage_se_reported = bool(re.search(r\"\\bSE\\b|standard error|\\(\\s*[\\d,.]+\\s*\\)\", wage_cell))\n\n# ---- derived\natt5, se5 = num(att5_s), num(se5_s)\nci_lo = round(att5 - Z * se5, 4)\nci_hi = round(att5 + Z * se5, 4)\nwlo, whi = num(w_lo), num(w_hi)\nwage_se = round((whi - wlo) / (2 * Z), 2)\n\nres = {\n    \"task_id\": \"F4-conf-01\",\n    \"dcr_att_5km\": att5, \"dcr_att_5km_se\": se5,\n    \"dcr_att_3km\": num(att3_s), \"dcr_att_3km_se\": num(se3_s),\n    \"dcr_obs_5km\": obs5,\n    \"dcr_narcotics_att_3km\": num(narc_s), \"dcr_narcotics_att_3km_se\": num(narc_se_s),\n    \"dcr_fatal_att_1km\": num(fatal_s), \"dcr_fatal_att_1km_se\": num(fatal_se_s),\n    \"dcr_hiv_att_5km\": num(hiv_s), \"dcr_hiv_att_5km_se\": num(hiv_se_s),\n    \"dcr_hiv_att_5km_p_value\": hiv_p,\n    \"dcr_hiv_att_5km_significant_10pct\": hiv_sig10,\n    \"dcr_att_5km_ci_low\": ci_lo, \"dcr_att_5km_ci_high\": ci_hi,\n    \"ri_wage_ate\": num(w_pt), \"ri_wage_ate_ci_low\": wlo, \"ri_wage_ate_ci_high\": whi,\n    \"ri_wage_ate_se_reported\": wage_se_reported,\n    \"ri_wage_ate_se_derived\": wage_se,\n    \"ri_control_mean_wage\": ctrl_mean,\n    \"ri_reemployment_ate_pp\": num(r_pt),\n    \"ri_weeks_ui_ate\": num(u_pt), \"ri_weeks_ui_ate_ci_low\": num(u_lo), \"ri_weeks_ui_ate_ci_high\": num(u_hi),\n}\n\nL6 = f\"{DCR_ID} | Table 6\"\nL7 = f\"{DCR_ID} | Table 7\"\nL2 = f\"{RI_ID} | Table 2\"\nres[\"locators\"] = {\n    \"dcr_att_5km\": f'{L6} | row \"ATT\" | column \"5 km\"',\n    \"dcr_att_5km_se\": f'{L6} | row \"ATT\" (standard-error line in parentheses) | column \"5 km\"',\n    \"dcr_att_3km\": f'{L6} | row \"ATT\" | column \"3 km\"',\n    \"dcr_att_3km_se\": f'{L6} | row \"ATT\" (standard-error line in parentheses) | column \"3 km\"',\n    \"dcr_obs_5km\": f'{L6} | row \"Observations\" | column \"5 km\"',\n    \"dcr_narcotics_att_3km\": f'{L7} | row \"Narcotics\" | column \"3 km\"',\n    \"dcr_narcotics_att_3km_se\": f'{L7} | row \"Narcotics\" (standard-error line in parentheses) | column \"3 km\"',\n    \"dcr_fatal_att_1km\": f'{L7} | row \"Drug-related (fatal)\" | column \"1 km\"',\n    \"dcr_fatal_att_1km_se\": f'{L7} | row \"Drug-related (fatal)\" (standard-error line in parentheses) | column \"1 km\"',\n    \"dcr_hiv_att_5km\": f'{L7} | row \"HIV\" | column \"5 km\"',\n    \"dcr_hiv_att_5km_se\": f'{L7} | row \"HIV\" (standard-error line in parentheses) | column \"5 km\"',\n    \"dcr_hiv_att_5km_p_value\": f\"not reported in {DCR_ID} (Table 7 prints only significance stars, no exact p-values; an exact p-value could only be derived under an additional distributional assumption)\",\n    \"dcr_att_5km_ci_low\": f'{L6} | row \"ATT\" | column \"5 km\" | derived from printed ATT and SE (ATT - 1.96*SE)',\n    \"dcr_att_5km_ci_high\": f'{L6} | row \"ATT\" | column \"5 km\" | derived from printed ATT and SE (ATT + 1.96*SE)',\n    \"ri_wage_ate\": f'{L2} | row \"Wage\" | column \"Average treatment effect estimate\" (point estimate)',\n    \"ri_wage_ate_ci_low\": f'{L2} | row \"Wage\" | column \"Average treatment effect estimate\" (95% CI lower bound)',\n    \"ri_wage_ate_ci_high\": f'{L2} | row \"Wage\" | column \"Average treatment effect estimate\" (95% CI upper bound)',\n    \"ri_wage_ate_se_derived\": f'{L2} | row \"Wage\" | column \"Average treatment effect estimate\" | derived from printed 95% CI ((high - low)/(2*1.96)); SE not reported in {RI_ID}',\n    \"ri_control_mean_wage\": f'{L2} | row \"Wage\" | column \"Control\" (mean)',\n    \"ri_reemployment_ate_pp\": f'{L2} | row \"Reemployment\" | column \"Average treatment effect estimate\" (point estimate)',\n    \"ri_weeks_ui_ate\": f'{L2} | row \"Weeks on UI\" | column \"Average treatment effect estimate\" (point estimate)',\n    \"ri_weeks_ui_ate_ci_low\": f'{L2} | row \"Weeks on UI\" | column \"Average treatment effect estimate\" (95% CI lower bound)',\n    \"ri_weeks_ui_ate_ci_high\": f'{L2} | row \"Weeks on UI\" | column \"Average treatment effect estimate\" (95% CI upper bound)',\n}\nres[\"derivation_notes\"] = {\n    \"dcr_att_5km_ci\": \"Normal (Wald) approximation with critical value 1.96: ATT +/- 1.96*SE using Table 6 printed ATT -0.431 and clustered SE 0.210; bounds rounded to 4 decimals. Assumes asymptotic normality of the Callaway-Sant'Anna estimator; the paper's own (event-study) intervals are also described as 95% CIs from clustered SEs but no CI for the overall ATT is printed.\",\n    \"ri_wage_ate_se\": \"SE = (upper - lower)/(2*1.96) from the Table 2 printed 95% CI [166, 2,141]; rounded to 2 decimals. Assumes the CI is a symmetric normal-approximation (Wald) interval; the printed CI is very nearly symmetric about $1,153 (987 below, 988 above). With n = 23,549 a t critical value is indistinguishable from 1.96.\",\n}\n# consistency checks -> data issues\nissues = []\nif (a19_att.replace(\"*\", \"\"), a19_se) == (att5_s.replace(\"*\", \"\"), se5_s):\n    pass\nelse:\n    issues.append({\"issue\": f\"Internal inconsistency in {DCR_ID}: the 5 km CS-DR ATT {att5_s} has SE {se5_s} in Table 6 but SE {a19_se} in Appendix Table A19 (same estimator, buffer, window and N=17,250).\",\n                   \"handling\": \"Used Table 6 as required by the key definitions; derived CI uses the Table 6 SE 0.210. Flagged; not resolved (source does not explain).\"})\nif \"640 regions\" in dcr:\n    issues.append({\"issue\": f\"Internal inconsistency in {DCR_ID}: Tables 6/7 and the Figure 6 note report 608 never-treated control regions, but the Table A19 note says 640 never-treated comparison regions.\",\n                   \"handling\": \"No requested key depends on it; flagged only.\"})\nif \"fall by 35%\" in dcr and \"approximately 30% at 5 km\" in dcr:\n    issues.append({\"issue\": f\"Text inconsistency in {DCR_ID}: abstract says fatal hospitalisations fall by 35%, Section 6.1 says ~30% at 5 km (Table 7 fatal 5 km ATT -0.354 implies exp(-0.354)-1 = -29.8%). Section 6 text also calls HIV estimates 'close to zero' although the 5 km point estimate is -0.340.\",\n                   \"handling\": \"Narrative only; all keys taken from the tables. Flagged.\"})\nif \"[1.75 weeks, 2.24 weeks]\" in ri:\n    issues.append({\"issue\": f\"Sign convention differs in {RI_ID}: text reports the weeks-on-UI CI as magnitudes [1.75, 2.24] of a reduction, Table 2 prints the ATE -1.99 with CI [-2.24, -1.75].\",\n                   \"handling\": \"Used Table 2 signed values as printed (lower bound first).\"})\nif \"p ¡ 0.001\" in ri:\n    issues.append({\"issue\": f\"Rendering defect in {RI_ID} text: 'p ¡ 0.001' (should read p < 0.001).\",\n                   \"handling\": \"Does not affect any key; flagged.\"})\nissues.append({\"issue\": f\"{RI_ID} Table 2 was re-set by the extract editor (one row per outcome; p-values reattached to columns) per the extract header.\",\n               \"handling\": \"Accepted the editor's layout; Table 3 'Difference in means' column duplicates Table 2 ATE values exactly (checked), supporting correct reattachment.\"})\nt3w = next(l for l in ri_lines if l.startswith(\"Wage | $1,153\"))\nassert cells(t3w)[1] == wage_cell, \"Table 3 / Table 2 wage mismatch\"\nres[\"data_issues\"] = issues\nres[\"feasibility\"] = \"complete\"\nres[\"feasibility_reason\"] = None\nres[\"n_input_rows\"] = n_input_rows\nvalue_keys = [k for k in res if k.startswith((\"dcr_\", \"ri_\"))]\nres[\"n_analysed\"] = len(value_keys)\nres[\"random_seed\"] = None\n\nos.makedirs(os.path.join(ROOT, \"out\"), exist_ok=True)\nwith open(os.path.join(ROOT, \"out\", \"results.json\"), \"w\") as f:\n    json.dump(res, f, indent=2, ensure_ascii=False)\nprint(json.dumps({k: res[k] for k in value_keys}, indent=1))\nEOF\n~/wp29/venv/bin/python out/analysis.py","description":"Write and run analysis script"}}]}}37{"type":"api-request","sessionId":"6a59845e-54b1-4582-86f5-752e1f4df7e4","id":"4f0faa0d-33bf-426d-9fd2-effc2cd1083e","timestamp":"2026-10-30T05:26:41.271Z","version":"2.1.314","querySource":"sdk","shapeHash":"48ce5b29cfebf0ef61214c91a04304198dbc6c218e84d6eb22b1863e5edbbfe3","params":{"model":"claude-opus-5-5","betas":["claude-code-20250219","interleaved-thinking-2025-05-14","context-management-2025-06-27","prompt-caching-scope-2026-01-05","per-turn-control-2026-07-01","thinking-token-count-2026-05-13","mid-conversation-system-clear-at-2026-08-21","effort-2025-11-24","thinking-binding-controls-2026-08-01","thinking-display-updates-2026-08-18","cache-diagnosis-2026-04-07"],"max_tokens":128000,"thinking":{"type":"adaptive","display":"updates"},"context_management":{"edits":[{"type":"clear_thinking_20251015","keep":"all"}]},"output_config":{"effort":"medium"}},"base":"055d00a5-85a4-4493-a754-67cc1378bbef","keep":8,"tail":["9ae32cc0db47853ed7ae0dfa7271215d269b51c0846274f497a450016cd48561","ab673e6a47253737b6ceb618fcd0a29ed09dfe37bd7c4d2e0e4a330305903c25"],"cacheControl":[{"path":["system",0],"value":{"type":"ephemeral"}},{"path":["system",1],"value":{"type":"ephemeral"}},{"path":["messages",9,"content",0],"value":{"type":"ephemeral"}}],"requestDigest":"1a47138418d617f55bce3b9e94c02ccb1a77d3226d80e95b1fe5990ae220eaad"}38{"type":"last-prompt","lastPrompt":"Begin","leafUuid":"6ba13ef7-c036-4134-814f-f98c70ad1deb","sessionId":"6a59845e-54b1-4582-86f5-752e1f4df7e4"}39{"type":"atis-latch","atis":"","sessionId":"6a59845e-54b1-4582-86f5-752e1f4df7e4"}40{"parentUuid":"6ba13ef7-c036-4134-814f-f98c70ad1deb","isSidechain":false,"message":{"model":"claude-opus-5-5","id":"msg_011CgXsFauMm16sEY6cCoqY5","type":"message","role":"assistant","content":[{"type":"text","text":"The earlier `cd` persisted; 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derive two quantities.\nReads only data/; writes out/results.json deterministically. No randomness.\"\"\"\nimport json, re, os, hashlib\n\nROOT = os.path.dirname(os.path.dirname(os.path.abspath(__file__)))\nD = os.path.join(ROOT, \"data\")\nDCR_ID, RI_ID = \"arxiv-2609.27732v1\", \"arxiv-2606.14621v1\"\nZ = 1.96  # normal critical value for two-sided 95% intervals\n\n\ndef read(rel):\n    with open(os.path.join(D, rel), encoding=\"utf-8\") as f:\n        return f.read()\n\n\ndef wc_l(text):\n    return text.count(\"\\n\")\n\n\nreadme = read(\"README.md\")\ndcr = read(f\"sources/{DCR_ID}.txt\")\nri = read(f\"sources/{RI_ID}.txt\")\nn_input_rows = {\"README.md\": wc_l(readme), f\"sources/{DCR_ID}.txt\": wc_l(dcr),\n                f\"sources/{RI_ID}.txt\": wc_l(ri)}\n\n# verify SHA-256 against README provenance table\nfor name, txt in [(DCR_ID, dcr), (RI_ID, ri)]:\n    h = hashlib.sha256(txt.encode(\"utf-8\")).hexdigest()\n    assert h in readme, f\"SHA mismatch for {name}\"\n    print(f\"[sha256 ok] {name} {h}\")\n\ndcr_lines = [l for l in dcr.split(\"\\n\") if l.strip()]\n\n\ndef table_block(lines, start_pat, end_pat):\n    i = next(k for k, l in enumerate(lines) if l.startswith(start_pat))\n    j = next(k for k, l in enumerate(lines) if k > i and l.startswith(end_pat))\n    return lines[i:j]\n\n\ndef cells(line):\n    return [c.strip() for c in line.split(\"|\")]\n\n\ndef num(s):\n    \"\"\"Parse a printed number: strip stars, parentheses, $, commas, %; keep sign.\"\"\"\n    t = s.replace(\"$\", \"\").replace(\",\", \"\").replace(\"*\", \"\").strip(\"() %\")\n    return float(t)\n\n\ndef coef_and_se(block, row_label, col_label, header_idx=0):\n    header = cells(block[header_idx])\n    col = header.index(col_label)\n    k = next(k for k, l in enumerate(block) if cells(l)[0] == row_label)\n    row, se_row = cells(block[k]), cells(block[k + 1])\n    assert se_row[0] == \"\" and se_row[col].startswith(\"(\"), se_row\n    print(f\"[match] row: {block[k]!r}\\n        se : {block[k+1]!r}  -> col {col_label!r}: {row[col]} / {se_row[col]}\")\n    return row[col], se_row[col]\n\n\n# ---- DCR Table 6\nt6 = table_block(dcr_lines, \"Table 6:\", \"Note:\")[1:]\natt5_s, se5_s = coef_and_se(t6, \"ATT\", \"5 km\")\natt3_s, se3_s = coef_and_se(t6, \"ATT\", \"3 km\")\nhdr6 = cells(t6[0])\nobs_line = next(l for l in t6 if cells(l)[0] == \"Observations\")\nprint(f\"[match] {obs_line!r}\")\nobs5 = int(cells(obs_line)[hdr6.index(\"5 km\")].replace(\",\", \"\"))\n\n# ---- DCR Table 7\nt7 = table_block(dcr_lines, \"Table 7:\", \"Note:\")[1:]\nnarc_s, narc_se_s = coef_and_se(t7, \"Narcotics\", \"3 km\")\nfatal_s, fatal_se_s = coef_and_se(t7, \"Drug-related (fatal)\", \"1 km\")\nhiv_s, hiv_se_s = coef_and_se(t7, \"HIV\", \"5 km\")\nhiv_sig10 = \"*\" in hiv_s  # any star = p<0.10 per table note\n# exact p-value: does the extract print one for Table 7 cells?\nt7_has_p = any(re.search(r\"p\\s*=\\s*0?\\.\\d\", l) for l in t7)\nhiv_p = None\nprint(f\"[check] Table 7 prints exact p-values: {t7_has_p}\")\nassert not t7_has_p\n\n# ---- Table A19 (only used for consistency check)\nta19 = table_block(dcr_lines, \"Table A19:\", \"Note:\")[1:]\na19_att, a19_se = coef_and_se(ta19, \"ATT\", \"CS-DR\")\n\n# ---- RI Table 2\nri_lines = ri.split(\"\\n\")\nhdr_idx = next(k for k, l in enumerate(ri_lines) if l.startswith(\"Outcome | Treatment | Control\"))\nhdr2 = cells(ri_lines[hdr_idx])\nt2 = {}\nfor l in ri_lines[hdr_idx + 1:]:\n    if not l.strip():\n        break\n    c = cells(l)\n    t2[c[0]] = dict(zip(hdr2, c))\nATE = \"Average treatment effect estimate\"\n\n\ndef parse_effect(cell):\n    m = re.match(r\"^(-?\\$?-?[\\d,]*\\d(?:\\.\\d+)?)\\s*(?:pp|weeks|wks)?;\\s*p\\s*([=<])\\s*([\\d.]+);\\s*95% CI:\\s*\\[(-?[\\d.]+(?:,\\d{3})*),\\s*(-?[\\d.]+(?:,\\d{3})*)\\]$\", cell)\n    assert m, cell\n    return m.group(1), m.group(2) + m.group(3), m.group(4), m.group(5)\n\n\nwage_cell = t2[\"Wage\"][ATE]\nreemp_cell = t2[\"Reemployment\"][ATE]\nweeks_cell = t2[\"Weeks on UI\"][ATE]\nctrl_cell = t2[\"Wage\"][\"Control\"]\nfor lab in [\"Wage\", \"Reemployment\", \"Weeks on UI\"]:\n    print(f\"[match] Table 2 {lab!r} ATE cell: {t2[lab][ATE]!r}\")\nprint(f\"[match] Table 2 'Wage' Control cell: {ctrl_cell!r}\")\nw_pt, w_p, w_lo, w_hi = parse_effect(wage_cell)\nr_pt, r_p, r_lo, r_hi = parse_effect(reemp_cell)\nu_pt, u_p, u_lo, u_hi = parse_effect(weeks_cell)\nctrl_mean = num(re.match(r\"^(\\$[\\d,]+)\", ctrl_cell).group(1))\nwage_se_reported = bool(re.search(r\"\\bSE\\b|standard error|\\(\\s*[\\d,.]+\\s*\\)\", wage_cell))\n\n# ---- derived\natt5, se5 = num(att5_s), num(se5_s)\nci_lo = round(att5 - Z * se5, 4)\nci_hi = round(att5 + Z * se5, 4)\nwlo, whi = num(w_lo), num(w_hi)\nwage_se = round((whi - wlo) / (2 * Z), 2)\n\nres = {\n    \"task_id\": \"F4-conf-01\",\n    \"dcr_att_5km\": att5, \"dcr_att_5km_se\": se5,\n    \"dcr_att_3km\": num(att3_s), \"dcr_att_3km_se\": num(se3_s),\n    \"dcr_obs_5km\": obs5,\n    \"dcr_narcotics_att_3km\": num(narc_s), \"dcr_narcotics_att_3km_se\": num(narc_se_s),\n    \"dcr_fatal_att_1km\": num(fatal_s), \"dcr_fatal_att_1km_se\": num(fatal_se_s),\n    \"dcr_hiv_att_5km\": num(hiv_s), \"dcr_hiv_att_5km_se\": num(hiv_se_s),\n    \"dcr_hiv_att_5km_p_value\": hiv_p,\n    \"dcr_hiv_att_5km_significant_10pct\": hiv_sig10,\n    \"dcr_att_5km_ci_low\": ci_lo, \"dcr_att_5km_ci_high\": ci_hi,\n    \"ri_wage_ate\": num(w_pt), \"ri_wage_ate_ci_low\": wlo, \"ri_wage_ate_ci_high\": whi,\n    \"ri_wage_ate_se_reported\": wage_se_reported,\n    \"ri_wage_ate_se_derived\": wage_se,\n    \"ri_control_mean_wage\": ctrl_mean,\n    \"ri_reemployment_ate_pp\": num(r_pt),\n    \"ri_weeks_ui_ate\": num(u_pt), \"ri_weeks_ui_ate_ci_low\": num(u_lo), \"ri_weeks_ui_ate_ci_high\": num(u_hi),\n}\n\nL6 = f\"{DCR_ID} | Table 6\"\nL7 = f\"{DCR_ID} | Table 7\"\nL2 = f\"{RI_ID} | Table 2\"\nres[\"locators\"] = {\n    \"dcr_att_5km\": f'{L6} | row \"ATT\" | column \"5 km\"',\n    \"dcr_att_5km_se\": f'{L6} | row \"ATT\" (standard-error line in parentheses) | column \"5 km\"',\n    \"dcr_att_3km\": f'{L6} | row \"ATT\" | column \"3 km\"',\n    \"dcr_att_3km_se\": f'{L6} | row \"ATT\" (standard-error line in parentheses) | column \"3 km\"',\n    \"dcr_obs_5km\": f'{L6} | row \"Observations\" | column \"5 km\"',\n    \"dcr_narcotics_att_3km\": f'{L7} | row \"Narcotics\" | column \"3 km\"',\n    \"dcr_narcotics_att_3km_se\": f'{L7} | row \"Narcotics\" (standard-error line in parentheses) | column \"3 km\"',\n    \"dcr_fatal_att_1km\": f'{L7} | row \"Drug-related (fatal)\" | column \"1 km\"',\n    \"dcr_fatal_att_1km_se\": f'{L7} | row \"Drug-related (fatal)\" (standard-error line in parentheses) | column \"1 km\"',\n    \"dcr_hiv_att_5km\": f'{L7} | row \"HIV\" | column \"5 km\"',\n    \"dcr_hiv_att_5km_se\": f'{L7} | row \"HIV\" (standard-error line in parentheses) | column \"5 km\"',\n    \"dcr_hiv_att_5km_p_value\": f\"not reported in {DCR_ID} (Table 7 prints only significance stars, no exact p-values; an exact p-value could only be computed under an additional distributional assumption)\",\n    \"dcr_att_5km_ci_low\": f'{L6} | row \"ATT\" | column \"5 km\" | derived from printed ATT and SE (ATT - 1.96*SE)',\n    \"dcr_att_5km_ci_high\": f'{L6} | row \"ATT\" | column \"5 km\" | derived from printed ATT and SE (ATT + 1.96*SE)',\n    \"ri_wage_ate\": f'{L2} | row \"Wage\" | column \"Average treatment effect estimate\" (point estimate)',\n    \"ri_wage_ate_ci_low\": f'{L2} | row \"Wage\" | column \"Average treatment effect estimate\" (95% CI lower bound)',\n    \"ri_wage_ate_ci_high\": f'{L2} | row \"Wage\" | column \"Average treatment effect estimate\" (95% CI upper bound)',\n    \"ri_wage_ate_se_derived\": f'{L2} | row \"Wage\" | column \"Average treatment effect estimate\" | derived from printed 95% CI ((high - low)/(2*1.96)); SE not reported in {RI_ID}',\n    \"ri_control_mean_wage\": f'{L2} | row \"Wage\" | column \"Control\" (mean)',\n    \"ri_reemployment_ate_pp\": f'{L2} | row \"Reemployment\" | column \"Average treatment effect estimate\" (point estimate)',\n    \"ri_weeks_ui_ate\": f'{L2} | row \"Weeks on UI\" | column \"Average treatment effect estimate\" (point estimate)',\n    \"ri_weeks_ui_ate_ci_low\": f'{L2} | row \"Weeks on UI\" | column \"Average treatment effect estimate\" (95% CI lower bound)',\n    \"ri_weeks_ui_ate_ci_high\": f'{L2} | row \"Weeks on UI\" | column \"Average treatment effect estimate\" (95% CI upper bound)',\n}\nres[\"derivation_notes\"] = {\n    \"dcr_att_5km_ci\": \"Normal (Wald) approximation with critical value 1.96: ATT +/- 1.96*SE using the Table 6 printed ATT -0.431 and clustered SE 0.210; bounds rounded to 4 decimals. Assumes approximate normality of the Callaway-Sant'Anna estimator; the source prints no CI for the overall ATT.\",\n    \"ri_wage_ate_se\": \"SE = (upper - lower)/(2*1.96) from the Table 2 printed 95% CI [166, 2,141]; rounded to 2 decimals. Assumes the CI is a symmetric normal-approximation (Wald) interval; the printed CI is nearly symmetric about 1,153 (987 below, 988 above). With N = 23,549 a t critical value is practically identical to 1.96.\",\n}\n# consistency checks -> data issues\nissues = []\nif (a19_att.replace(\"*\", \"\"), a19_se) != (att5_s.replace(\"*\", \"\"), se5_s):\n    issues.append({\"issue\": f\"Internal inconsistency in {DCR_ID}: the 5 km CS-DR ATT {att5_s} has SE {se5_s} in Table 6 but SE {a19_se} in Appendix Table A19 (same estimator, buffer and N=17,250).\",\n                   \"handling\": \"Used Table 6 as required by the key definitions; the derived CI uses the Table 6 SE 0.210. Flagged; not resolvable from the extract.\"})\nif \"640 regions\" in dcr:\n    issues.append({\"issue\": f\"Internal inconsistency in {DCR_ID}: Tables 6/7 and the Figure 6 note report 608 never-treated control regions, but the Table A19 note says 640 never-treated comparison regions.\",\n                   \"handling\": \"No requested key depends on it; flagged only.\"})\nif \"fall by 35%\" in dcr and \"approximately 30% at 5 km\" in dcr:\n    issues.append({\"issue\": f\"Text inconsistency in {DCR_ID}: the abstract says fatal hospitalisations fall by 35%, Section 6.1 says ~30% at 5 km (Table 7 fatal 5 km ATT -0.354 implies exp(-0.354)-1 = -29.8%). Section 6 text also calls HIV estimates 'close to zero' although the 5 km point estimate is -0.340.\",\n                   \"handling\": \"Narrative only; all keys taken from the tables. Flagged.\"})\nif \"[1.75 weeks, 2.24 weeks]\" in ri:\n    issues.append({\"issue\": f\"Sign convention differs in {RI_ID}: the text reports the weeks-on-UI CI as magnitudes of a reduction [1.75, 2.24], while Table 2 prints the ATE -1.99 with CI [-2.24, -1.75].\",\n                   \"handling\": \"Used the Table 2 signed values as printed (lower bound first).\"})\nif \"p ¡ 0.001\" in ri:\n    issues.append({\"issue\": f\"Rendering defect in {RI_ID} text: 'p ¡ 0.001' (should read p < 0.001).\",\n                   \"handling\": \"Does not affect any key; flagged.\"})\nt3w = next(l for l in ri_lines if l.startswith(\"Wage | $1,153\"))\nassert cells(t3w)[1] == wage_cell, \"Table 3 / Table 2 wage mismatch\"\nissues.append({\"issue\": f\"{RI_ID} Table 2 was re-set by the extract editor (one row per outcome; p-values reattached to columns), per the extract header.\",\n               \"handling\": \"Accepted the editor's layout; the Table 3 'Difference in means' wage cell duplicates the Table 2 wage ATE cell exactly (checked in code), consistent with correct reattachment.\"})\nres[\"data_issues\"] = issues\nres[\"feasibility\"] = \"complete\"\nres[\"feasibility_reason\"] = None\nres[\"n_input_rows\"] = n_input_rows\nvalue_keys = [k for k in res if k.startswith((\"dcr_\", \"ri_\"))]\nres[\"n_analysed\"] = len(value_keys)\nres[\"random_seed\"] = None\n\nos.makedirs(os.path.join(ROOT, \"out\"), exist_ok=True)\nwith open(os.path.join(ROOT, \"out\", \"results.json\"), \"w\") as f:\n    json.dump(res, f, indent=2, ensure_ascii=False)\nprint(json.dumps({k: res[k] for k in value_keys}, indent=1))\nEOF\n~/wp29/venv/bin/python out/analysis.py","description":"Write and run analysis script from root"},"caller":{"type":"direct"}}],"container":null,"stop_reason":"tool_use","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":190,"cache_read_input_tokens":29494,"output_tokens":5950,"output_tokens_details":{"thinking_tokens":25},"server_tool_use":{"web_search_requests":0,"web_fetch_requests":0},"service_tier":"standard","cache_creation":{"ephemeral_1h_input_tokens":0,"ephemeral_5m_input_tokens":190},"inference_geo":"global","iterations":[],"speed":"standard","fallback_credit":null},"input_transformations":[],"diagnostics":null,"context_management":null},"apiBlockIndex":1,"requestId":"req_011CgXsFjHPLU3FAcK71o1kk","requestRef":"4f33b55d-9e9e-4e1f-bd66-103eac62ce72","requestedModel":"claude-opus-5-5","type":"assistant","uuid":"b0cfc3fb-b4c2-477a-8f7e-042673c8bd60","timestamp":"2026-10-30T05:27:22.435Z","effort":"medium","perTurnEffort":"medium","userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0288","sessionId":"6a59845e-54b1-4582-86f5-752e1f4df7e4","version":"2.1.314","gitBranch":"HEAD"}47{"parentUuid":"b0cfc3fb-b4c2-477a-8f7e-042673c8bd60","isSidechain":false,"promptId":"214a8ece-2bbc-459c-aa1a-66062e2a4650","type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_01Y1ZncQ2qHE5P5aXjQSPDCw","type":"tool_result","content":"[sha256 ok] arxiv-2609.27732v1 ee2d444d2f78f2af2ca2cf69b59a61fcd7ac5e10ea29e8ddcd67917bf07d6a8b\n[sha256 ok] arxiv-2606.14621v1 27c463c0a144d9d5a3916dc641f15b6029f6c68b5e574ad9074e7df1aeb0a238\n[match] row: 'ATT | -0.399 | -0.283 | -0.431**'\n        se : '| (0.412) | (0.279) | (0.210)'  -> col '5 km': -0.431** / (0.210)\n[match] row: 'ATT | -0.399 | -0.283 | -0.431**'\n        se : '| (0.412) | (0.279) | (0.210)'  -> col '3 km': -0.283 / (0.279)\n[match] 'Observations | 16,175 | 16,875 | 17,250'\n[match] row: 'Narcotics | 1.186*** | 0.549*** | 0.390**'\n        se : '| (0.288) | (0.187) | (0.165)'  -> col '3 km': 0.549*** / (0.187)\n[match] row: 'Drug-related (fatal) | -1.207*** | -0.290 | -0.354**'\n        se : '| (0.137) | (0.215) | (0.178)'  -> col '1 km': -1.207*** / (0.137)\n[match] row: 'HIV | 0.063 | -0.239 | -0.340'\n        se : '| (0.801) | (0.337) | (0.294)'  -> col '5 km': -0.340 / (0.294)\n[check] Table 7 prints exact p-values: False\n[match] row: 'ATT | -0.431** | -0.438** | -0.362* | -0.149***'\n        se : '| (0.218) | (0.208) | (0.205) | (0.038)'  -> col 'CS-DR': -0.431** / (0.218)\n[match] Table 2 'Wage' ATE cell: '$1,153; p=0.022; 95% CI: [166, 2,141]'\n[match] Table 2 'Reemployment' ATE cell: '1.47 pp; p=0.013; 95% CI: [0.32, 2.62]'\n[match] Table 2 'Weeks on UI' ATE cell: '-1.99 weeks; p<0.001; 95% CI: [-2.24, -1.75]'\n[match] Table 2 'Wage' Control cell: '$33,043 (SD: 36,231)'\n{\n \"dcr_att_5km\": -0.431,\n \"dcr_att_5km_se\": 0.21,\n \"dcr_att_3km\": -0.283,\n \"dcr_att_3km_se\": 0.279,\n \"dcr_obs_5km\": 17250,\n \"dcr_narcotics_att_3km\": 0.549,\n \"dcr_narcotics_att_3km_se\": 0.187,\n \"dcr_fatal_att_1km\": -1.207,\n \"dcr_fatal_att_1km_se\": 0.137,\n \"dcr_hiv_att_5km\": -0.34,\n \"dcr_hiv_att_5km_se\": 0.294,\n \"dcr_hiv_att_5km_p_value\": null,\n \"dcr_hiv_att_5km_significant_10pct\": false,\n \"dcr_att_5km_ci_low\": -0.8426,\n \"dcr_att_5km_ci_high\": -0.0194,\n \"ri_wage_ate\": 1153.0,\n \"ri_wage_ate_ci_low\": 166.0,\n \"ri_wage_ate_ci_high\": 2141.0,\n \"ri_wage_ate_se_reported\": false,\n \"ri_wage_ate_se_derived\": 503.83,\n \"ri_control_mean_wage\": 33043.0,\n \"ri_reemployment_ate_pp\": 1.47,\n \"ri_weeks_ui_ate\": -1.99,\n \"ri_weeks_ui_ate_ci_low\": -2.24,\n \"ri_weeks_ui_ate_ci_high\": -1.75\n}","is_error":false}]},"uuid":"c08404ac-5183-44f0-87d0-93d20e8ef860","timestamp":"2026-10-30T05:27:22.495Z","toolUseResult":{"stdout":"[sha256 ok] arxiv-2609.27732v1 ee2d444d2f78f2af2ca2cf69b59a61fcd7ac5e10ea29e8ddcd67917bf07d6a8b\n[sha256 ok] arxiv-2606.14621v1 27c463c0a144d9d5a3916dc641f15b6029f6c68b5e574ad9074e7df1aeb0a238\n[match] row: 'ATT | -0.399 | -0.283 | -0.431**'\n        se : '| (0.412) | (0.279) | (0.210)'  -> col '5 km': -0.431** / (0.210)\n[match] row: 'ATT | -0.399 | -0.283 | -0.431**'\n        se : '| (0.412) | (0.279) | (0.210)'  -> col '3 km': -0.283 / (0.279)\n[match] 'Observations | 16,175 | 16,875 | 17,250'\n[match] row: 'Narcotics | 1.186*** | 0.549*** | 0.390**'\n        se : '| (0.288) | (0.187) | (0.165)'  -> col '3 km': 0.549*** / (0.187)\n[match] row: 'Drug-related (fatal) | -1.207*** | -0.290 | -0.354**'\n        se : '| (0.137) | (0.215) | (0.178)'  -> col '1 km': -1.207*** / (0.137)\n[match] row: 'HIV | 0.063 | -0.239 | -0.340'\n        se : '| (0.801) | (0.337) | (0.294)'  -> col '5 km': -0.340 / (0.294)\n[check] Table 7 prints exact p-values: False\n[match] row: 'ATT | -0.431** | -0.438** | -0.362* | -0.149***'\n        se : '| (0.218) | (0.208) | (0.205) | (0.038)'  -> col 'CS-DR': -0.431** / (0.218)\n[match] Table 2 'Wage' ATE cell: '$1,153; p=0.022; 95% CI: [166, 2,141]'\n[match] Table 2 'Reemployment' ATE cell: '1.47 pp; p=0.013; 95% CI: [0.32, 2.62]'\n[match] Table 2 'Weeks on UI' ATE cell: '-1.99 weeks; p<0.001; 95% CI: [-2.24, -1.75]'\n[match] Table 2 'Wage' Control cell: '$33,043 (SD: 36,231)'\n{\n \"dcr_att_5km\": -0.431,\n \"dcr_att_5km_se\": 0.21,\n \"dcr_att_3km\": -0.283,\n \"dcr_att_3km_se\": 0.279,\n \"dcr_obs_5km\": 17250,\n \"dcr_narcotics_att_3km\": 0.549,\n \"dcr_narcotics_att_3km_se\": 0.187,\n \"dcr_fatal_att_1km\": -1.207,\n \"dcr_fatal_att_1km_se\": 0.137,\n \"dcr_hiv_att_5km\": -0.34,\n \"dcr_hiv_att_5km_se\": 0.294,\n \"dcr_hiv_att_5km_p_value\": null,\n \"dcr_hiv_att_5km_significant_10pct\": false,\n \"dcr_att_5km_ci_low\": -0.8426,\n \"dcr_att_5km_ci_high\": -0.0194,\n \"ri_wage_ate\": 1153.0,\n \"ri_wage_ate_ci_low\": 166.0,\n \"ri_wage_ate_ci_high\": 2141.0,\n \"ri_wage_ate_se_reported\": false,\n \"ri_wage_ate_se_derived\": 503.83,\n \"ri_control_mean_wage\": 33043.0,\n \"ri_reemployment_ate_pp\": 1.47,\n \"ri_weeks_ui_ate\": -1.99,\n \"ri_weeks_ui_ate_ci_low\": -2.24,\n \"ri_weeks_ui_ate_ci_high\": -1.75\n}","stderr":"","interrupted":false,"isImage":false,"noOutputExpected":false},"sandboxed":false,"sourceToolAssistantUUID":"b0cfc3fb-b4c2-477a-8f7e-042673c8bd60","permissionDecision":{"decision":"accept","source":"config","reasonType":"rule","ruleSource":"cliArg"},"userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0288","sessionId":"6a59845e-54b1-4582-86f5-752e1f4df7e4","version":"2.1.314","gitBranch":"HEAD"}48{"type":"api-request-blob","sessionId":"6a59845e-54b1-4582-86f5-752e1f4df7e4","hash":"ec89b234af4261a8950eae484d9849e21a64d56bacd37e5b905efb405707a996","message":{"role":"assistant","content":[{"type":"thinking","thinking":"","signature":"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"},{"type":"tool_use","id":"toolu_01Y1ZncQ2qHE5P5aXjQSPDCw","name":"Bash","input":{"command":"cat > out/analysis.py <<'EOF'\n\"\"\"F4-conf-01: extract printed values from two arXiv extracts; derive two quantities.\nReads only data/; writes out/results.json deterministically. No randomness.\"\"\"\nimport json, re, os, hashlib\n\nROOT = os.path.dirname(os.path.dirname(os.path.abspath(__file__)))\nD = os.path.join(ROOT, \"data\")\nDCR_ID, RI_ID = \"arxiv-2609.27732v1\", \"arxiv-2606.14621v1\"\nZ = 1.96  # normal critical value for two-sided 95% intervals\n\n\ndef read(rel):\n    with open(os.path.join(D, rel), encoding=\"utf-8\") as f:\n        return f.read()\n\n\ndef wc_l(text):\n    return text.count(\"\\n\")\n\n\nreadme = read(\"README.md\")\ndcr = read(f\"sources/{DCR_ID}.txt\")\nri = read(f\"sources/{RI_ID}.txt\")\nn_input_rows = {\"README.md\": wc_l(readme), f\"sources/{DCR_ID}.txt\": wc_l(dcr),\n                f\"sources/{RI_ID}.txt\": wc_l(ri)}\n\n# verify SHA-256 against README provenance table\nfor name, txt in [(DCR_ID, dcr), (RI_ID, ri)]:\n    h = hashlib.sha256(txt.encode(\"utf-8\")).hexdigest()\n    assert h in readme, f\"SHA mismatch for {name}\"\n    print(f\"[sha256 ok] {name} {h}\")\n\ndcr_lines = [l for l in dcr.split(\"\\n\") if l.strip()]\n\n\ndef table_block(lines, start_pat, end_pat):\n    i = next(k for k, l in enumerate(lines) if l.startswith(start_pat))\n    j = next(k for k, l in enumerate(lines) if k > i and l.startswith(end_pat))\n    return lines[i:j]\n\n\ndef cells(line):\n    return [c.strip() for c in line.split(\"|\")]\n\n\ndef num(s):\n    \"\"\"Parse a printed number: strip stars, parentheses, $, commas, %; keep sign.\"\"\"\n    t = s.replace(\"$\", \"\").replace(\",\", \"\").replace(\"*\", \"\").strip(\"() %\")\n    return float(t)\n\n\ndef coef_and_se(block, row_label, col_label, header_idx=0):\n    header = cells(block[header_idx])\n    col = header.index(col_label)\n    k = next(k for k, l in enumerate(block) if cells(l)[0] == row_label)\n    row, se_row = cells(block[k]), cells(block[k + 1])\n    assert se_row[0] == \"\" and se_row[col].startswith(\"(\"), se_row\n    print(f\"[match] row: {block[k]!r}\\n        se : {block[k+1]!r}  -> col {col_label!r}: {row[col]} / {se_row[col]}\")\n    return row[col], se_row[col]\n\n\n# ---- DCR Table 6\nt6 = table_block(dcr_lines, \"Table 6:\", \"Note:\")[1:]\natt5_s, se5_s = coef_and_se(t6, \"ATT\", \"5 km\")\natt3_s, se3_s = coef_and_se(t6, \"ATT\", \"3 km\")\nhdr6 = cells(t6[0])\nobs_line = next(l for l in t6 if cells(l)[0] == \"Observations\")\nprint(f\"[match] {obs_line!r}\")\nobs5 = int(cells(obs_line)[hdr6.index(\"5 km\")].replace(\",\", \"\"))\n\n# ---- DCR Table 7\nt7 = table_block(dcr_lines, \"Table 7:\", \"Note:\")[1:]\nnarc_s, narc_se_s = coef_and_se(t7, \"Narcotics\", \"3 km\")\nfatal_s, fatal_se_s = coef_and_se(t7, \"Drug-related (fatal)\", \"1 km\")\nhiv_s, hiv_se_s = coef_and_se(t7, \"HIV\", \"5 km\")\nhiv_sig10 = \"*\" in hiv_s  # any star = p<0.10 per table note\n# exact p-value: does the extract print one for Table 7 cells?\nt7_has_p = any(re.search(r\"p\\s*=\\s*0?\\.\\d\", l) for l in t7)\nhiv_p = None\nprint(f\"[check] Table 7 prints exact p-values: {t7_has_p}\")\nassert not t7_has_p\n\n# ---- Table A19 (only used for consistency check)\nta19 = table_block(dcr_lines, \"Table A19:\", \"Note:\")[1:]\na19_att, a19_se = coef_and_se(ta19, \"ATT\", \"CS-DR\")\n\n# ---- RI Table 2\nri_lines = ri.split(\"\\n\")\nhdr_idx = next(k for k, l in enumerate(ri_lines) if l.startswith(\"Outcome | Treatment | Control\"))\nhdr2 = cells(ri_lines[hdr_idx])\nt2 = {}\nfor l in ri_lines[hdr_idx + 1:]:\n    if not l.strip():\n        break\n    c = cells(l)\n    t2[c[0]] = dict(zip(hdr2, c))\nATE = \"Average treatment effect estimate\"\n\n\ndef parse_effect(cell):\n    m = re.match(r\"^(-?\\$?-?[\\d,]*\\d(?:\\.\\d+)?)\\s*(?:pp|weeks|wks)?;\\s*p\\s*([=<])\\s*([\\d.]+);\\s*95% CI:\\s*\\[(-?[\\d.]+(?:,\\d{3})*),\\s*(-?[\\d.]+(?:,\\d{3})*)\\]$\", cell)\n    assert m, cell\n    return m.group(1), m.group(2) + m.group(3), m.group(4), m.group(5)\n\n\nwage_cell = t2[\"Wage\"][ATE]\nreemp_cell = t2[\"Reemployment\"][ATE]\nweeks_cell = t2[\"Weeks on UI\"][ATE]\nctrl_cell = t2[\"Wage\"][\"Control\"]\nfor lab in [\"Wage\", \"Reemployment\", \"Weeks on UI\"]:\n    print(f\"[match] Table 2 {lab!r} ATE cell: {t2[lab][ATE]!r}\")\nprint(f\"[match] Table 2 'Wage' Control cell: {ctrl_cell!r}\")\nw_pt, w_p, w_lo, w_hi = parse_effect(wage_cell)\nr_pt, r_p, r_lo, r_hi = parse_effect(reemp_cell)\nu_pt, u_p, u_lo, u_hi = parse_effect(weeks_cell)\nctrl_mean = num(re.match(r\"^(\\$[\\d,]+)\", ctrl_cell).group(1))\nwage_se_reported = bool(re.search(r\"\\bSE\\b|standard error|\\(\\s*[\\d,.]+\\s*\\)\", wage_cell))\n\n# ---- derived\natt5, se5 = num(att5_s), num(se5_s)\nci_lo = round(att5 - Z * se5, 4)\nci_hi = round(att5 + Z * se5, 4)\nwlo, whi = num(w_lo), num(w_hi)\nwage_se = round((whi - wlo) / (2 * Z), 2)\n\nres = {\n    \"task_id\": \"F4-conf-01\",\n    \"dcr_att_5km\": att5, \"dcr_att_5km_se\": se5,\n    \"dcr_att_3km\": num(att3_s), \"dcr_att_3km_se\": num(se3_s),\n    \"dcr_obs_5km\": obs5,\n    \"dcr_narcotics_att_3km\": num(narc_s), \"dcr_narcotics_att_3km_se\": num(narc_se_s),\n    \"dcr_fatal_att_1km\": num(fatal_s), \"dcr_fatal_att_1km_se\": num(fatal_se_s),\n    \"dcr_hiv_att_5km\": num(hiv_s), \"dcr_hiv_att_5km_se\": num(hiv_se_s),\n    \"dcr_hiv_att_5km_p_value\": hiv_p,\n    \"dcr_hiv_att_5km_significant_10pct\": hiv_sig10,\n    \"dcr_att_5km_ci_low\": ci_lo, \"dcr_att_5km_ci_high\": ci_hi,\n    \"ri_wage_ate\": num(w_pt), \"ri_wage_ate_ci_low\": wlo, \"ri_wage_ate_ci_high\": whi,\n    \"ri_wage_ate_se_reported\": wage_se_reported,\n    \"ri_wage_ate_se_derived\": wage_se,\n    \"ri_control_mean_wage\": ctrl_mean,\n    \"ri_reemployment_ate_pp\": num(r_pt),\n    \"ri_weeks_ui_ate\": num(u_pt), \"ri_weeks_ui_ate_ci_low\": num(u_lo), \"ri_weeks_ui_ate_ci_high\": num(u_hi),\n}\n\nL6 = f\"{DCR_ID} | Table 6\"\nL7 = f\"{DCR_ID} | Table 7\"\nL2 = f\"{RI_ID} | Table 2\"\nres[\"locators\"] = {\n    \"dcr_att_5km\": f'{L6} | row \"ATT\" | column \"5 km\"',\n    \"dcr_att_5km_se\": f'{L6} | row \"ATT\" (standard-error line in parentheses) | column \"5 km\"',\n    \"dcr_att_3km\": f'{L6} | row \"ATT\" | column \"3 km\"',\n    \"dcr_att_3km_se\": f'{L6} | row \"ATT\" (standard-error line in parentheses) | column \"3 km\"',\n    \"dcr_obs_5km\": f'{L6} | row \"Observations\" | column \"5 km\"',\n    \"dcr_narcotics_att_3km\": f'{L7} | row \"Narcotics\" | column \"3 km\"',\n    \"dcr_narcotics_att_3km_se\": f'{L7} | row \"Narcotics\" (standard-error line in parentheses) | column \"3 km\"',\n    \"dcr_fatal_att_1km\": f'{L7} | row \"Drug-related (fatal)\" | column \"1 km\"',\n    \"dcr_fatal_att_1km_se\": f'{L7} | row \"Drug-related (fatal)\" (standard-error line in parentheses) | column \"1 km\"',\n    \"dcr_hiv_att_5km\": f'{L7} | row \"HIV\" | column \"5 km\"',\n    \"dcr_hiv_att_5km_se\": f'{L7} | row \"HIV\" (standard-error line in parentheses) | column \"5 km\"',\n    \"dcr_hiv_att_5km_p_value\": f\"not reported in {DCR_ID} (Table 7 prints only significance stars, no exact p-values; an exact p-value could only be computed under an additional distributional assumption)\",\n    \"dcr_att_5km_ci_low\": f'{L6} | row \"ATT\" | column \"5 km\" | derived from printed ATT and SE (ATT - 1.96*SE)',\n    \"dcr_att_5km_ci_high\": f'{L6} | row \"ATT\" | column \"5 km\" | derived from printed ATT and SE (ATT + 1.96*SE)',\n    \"ri_wage_ate\": f'{L2} | row \"Wage\" | column \"Average treatment effect estimate\" (point estimate)',\n    \"ri_wage_ate_ci_low\": f'{L2} | row \"Wage\" | column \"Average treatment effect estimate\" (95% CI lower bound)',\n    \"ri_wage_ate_ci_high\": f'{L2} | row \"Wage\" | column \"Average treatment effect estimate\" (95% CI upper bound)',\n    \"ri_wage_ate_se_derived\": f'{L2} | row \"Wage\" | column \"Average treatment effect estimate\" | derived from printed 95% CI ((high - low)/(2*1.96)); SE not reported in {RI_ID}',\n    \"ri_control_mean_wage\": f'{L2} | row \"Wage\" | column \"Control\" (mean)',\n    \"ri_reemployment_ate_pp\": f'{L2} | row \"Reemployment\" | column \"Average treatment effect estimate\" (point estimate)',\n    \"ri_weeks_ui_ate\": f'{L2} | row \"Weeks on UI\" | column \"Average treatment effect estimate\" (point estimate)',\n    \"ri_weeks_ui_ate_ci_low\": f'{L2} | row \"Weeks on UI\" | column \"Average treatment effect estimate\" (95% CI lower bound)',\n    \"ri_weeks_ui_ate_ci_high\": f'{L2} | row \"Weeks on UI\" | column \"Average treatment effect estimate\" (95% CI upper bound)',\n}\nres[\"derivation_notes\"] = {\n    \"dcr_att_5km_ci\": \"Normal (Wald) approximation with critical value 1.96: ATT +/- 1.96*SE using the Table 6 printed ATT -0.431 and clustered SE 0.210; bounds rounded to 4 decimals. Assumes approximate normality of the Callaway-Sant'Anna estimator; the source prints no CI for the overall ATT.\",\n    \"ri_wage_ate_se\": \"SE = (upper - lower)/(2*1.96) from the Table 2 printed 95% CI [166, 2,141]; rounded to 2 decimals. Assumes the CI is a symmetric normal-approximation (Wald) interval; the printed CI is nearly symmetric about 1,153 (987 below, 988 above). With N = 23,549 a t critical value is practically identical to 1.96.\",\n}\n# consistency checks -> data issues\nissues = []\nif (a19_att.replace(\"*\", \"\"), a19_se) != (att5_s.replace(\"*\", \"\"), se5_s):\n    issues.append({\"issue\": f\"Internal inconsistency in {DCR_ID}: the 5 km CS-DR ATT {att5_s} has SE {se5_s} in Table 6 but SE {a19_se} in Appendix Table A19 (same estimator, buffer and N=17,250).\",\n                   \"handling\": \"Used Table 6 as required by the key definitions; the derived CI uses the Table 6 SE 0.210. Flagged; not resolvable from the extract.\"})\nif \"640 regions\" in dcr:\n    issues.append({\"issue\": f\"Internal inconsistency in {DCR_ID}: Tables 6/7 and the Figure 6 note report 608 never-treated control regions, but the Table A19 note says 640 never-treated comparison regions.\",\n                   \"handling\": \"No requested key depends on it; flagged only.\"})\nif \"fall by 35%\" in dcr and \"approximately 30% at 5 km\" in dcr:\n    issues.append({\"issue\": f\"Text inconsistency in {DCR_ID}: the abstract says fatal hospitalisations fall by 35%, Section 6.1 says ~30% at 5 km (Table 7 fatal 5 km ATT -0.354 implies exp(-0.354)-1 = -29.8%). Section 6 text also calls HIV estimates 'close to zero' although the 5 km point estimate is -0.340.\",\n                   \"handling\": \"Narrative only; all keys taken from the tables. Flagged.\"})\nif \"[1.75 weeks, 2.24 weeks]\" in ri:\n    issues.append({\"issue\": f\"Sign convention differs in {RI_ID}: the text reports the weeks-on-UI CI as magnitudes of a reduction [1.75, 2.24], while Table 2 prints the ATE -1.99 with CI [-2.24, -1.75].\",\n                   \"handling\": \"Used the Table 2 signed values as printed (lower bound first).\"})\nif \"p ¡ 0.001\" in ri:\n    issues.append({\"issue\": f\"Rendering defect in {RI_ID} text: 'p ¡ 0.001' (should read p < 0.001).\",\n                   \"handling\": \"Does not affect any key; flagged.\"})\nt3w = next(l for l in ri_lines if l.startswith(\"Wage | $1,153\"))\nassert cells(t3w)[1] == wage_cell, \"Table 3 / Table 2 wage mismatch\"\nissues.append({\"issue\": f\"{RI_ID} Table 2 was re-set by the extract editor (one row per outcome; p-values reattached to columns), per the extract header.\",\n               \"handling\": \"Accepted the editor's layout; the Table 3 'Difference in means' wage cell duplicates the Table 2 wage ATE cell exactly (checked in code), consistent with correct reattachment.\"})\nres[\"data_issues\"] = issues\nres[\"feasibility\"] = \"complete\"\nres[\"feasibility_reason\"] = None\nres[\"n_input_rows\"] = n_input_rows\nvalue_keys = [k for k in res if k.startswith((\"dcr_\", \"ri_\"))]\nres[\"n_analysed\"] = len(value_keys)\nres[\"random_seed\"] = None\n\nos.makedirs(os.path.join(ROOT, \"out\"), exist_ok=True)\nwith open(os.path.join(ROOT, \"out\", \"results.json\"), \"w\") as f:\n    json.dump(res, f, indent=2, ensure_ascii=False)\nprint(json.dumps({k: res[k] for k in value_keys}, indent=1))\nEOF\n~/wp29/venv/bin/python out/analysis.py","description":"Write and run analysis script from 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\"dcr_hiv_att_5km_significant_10pct\": false,\n \"dcr_att_5km_ci_low\": -0.8426,\n \"dcr_att_5km_ci_high\": -0.0194,\n \"ri_wage_ate\": 1153,\n \"ri_wage_ate_ci_low\": 166,\n \"ri_wage_ate_ci_high\": 2141,\n \"ri_wage_ate_se_reported\": false,\n \"ri_wage_ate_se_derived\": 503.83,\n \"ri_control_mean_wage\": 33043,\n \"ri_reemployment_ate_pp\": 1.47,\n \"ri_weeks_ui_ate\": -1.99,\n \"ri_weeks_ui_ate_ci_low\": -2.24,\n \"ri_weeks_ui_ate_ci_high\": -1.75\n}\n92\n25","is_error":false}]},"uuid":"5cac5913-cadf-4f73-92c6-642a0d0bf633","timestamp":"2026-10-30T05:27:26.990Z","toolUseResult":{"stdout":" \"dcr_hiv_att_5km_significant_10pct\": false,\n \"dcr_att_5km_ci_low\": -0.8426,\n \"dcr_att_5km_ci_high\": -0.0194,\n \"ri_wage_ate\": 1153,\n \"ri_wage_ate_ci_low\": 166,\n \"ri_wage_ate_ci_high\": 2141,\n \"ri_wage_ate_se_reported\": false,\n \"ri_wage_ate_se_derived\": 503.83,\n \"ri_control_mean_wage\": 33043,\n \"ri_reemployment_ate_pp\": 1.47,\n \"ri_weeks_ui_ate\": -1.99,\n 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the P1/P2 artifacts, checks, and gate run."}],"container":null,"stop_reason":"tool_use","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":693,"cache_read_input_tokens":37025,"output_tokens":2262,"output_tokens_details":{"thinking_tokens":0},"server_tool_use":{"web_search_requests":0,"web_fetch_requests":0},"service_tier":"standard","cache_creation":{"ephemeral_1h_input_tokens":0,"ephemeral_5m_input_tokens":693},"inference_geo":"global","iterations":[],"speed":"standard","fallback_credit":null},"input_transformations":[],"diagnostics":null,"context_management":null},"apiBlockIndex":0,"requestId":"req_011CgXsJx7tdK7KbRewF29tc","requestRef":"e394ee9b-aea9-417e-83c7-9818e55f6629","requestedModel":"claude-opus-5-5","type":"assistant","uuid":"a93a34af-2270-4267-9c33-54a8f71effd9","timestamp":"2026-10-30T05:27:28.325Z","effort":"medium","perTurnEffort":"medium","userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0288","sessionId":"6a59845e-54b1-4582-86f5-752e1f4df7e4","version":"2.1.314","gitBranch":"HEAD"}58{"parentUuid":"a93a34af-2270-4267-9c33-54a8f71effd9","isSidechain":false,"message":{"model":"claude-opus-5-5","id":"msg_011CgXsJxPmfJ7MxwonUJr5u","type":"message","role":"assistant","content":[{"type":"tool_use","id":"toolu_011cR2P8Rx1guyFTnLCHpUgk","name":"Bash","input":{"command":"cat > out/state/data_audit.md <<'EOF'\n# Data audit (P1)\n| file | SHA-256 | lines (wc -l) | matches README |\n|---|---|---|---|\n| data/README.md | 16ebdff389eeaee9508fe81c4edc43690f31f3463c469680c814134086ef9929 | 10 | (matches bundle.json) |\n| data/sources/arxiv-2609.27732v1.txt | ee2d444d2f78f2af2ca2cf69b59a61fcd7ac5e10ea29e8ddcd67917bf07d6a8b | 134 | yes |\n| data/sources/arxiv-2606.14621v1.txt | 27c463c0a144d9d5a3916dc641f15b6029f6c68b5e574ad9074e7df1aeb0a238 | 58 | yes |\n\nSchema: plain-text extracts; tables one row per line, \" | \" separators; DCR regression tables have SE line (parentheses) after each coefficient row. RESEA Tables 2–3 re-set one row per outcome with \"estimate; p=...; 95% CI: [lo, hi]\" cells.\nUnits: DCR ATTs in log points (outcome log hospitalisation rate per 100,000); Observations = region-years. RESEA wages annualised USD, reemployment % / pp, weeks on UI in weeks.\nDate ranges: DCR openings 1998–2022; RESEA RCT Feb 2022 – Sep 2023.\nMissing / duplicates: no missing cells in requested rows. Duplicates: RESEA Table 3 col 1 duplicates Table 2 ATE column (identical, checked). DCR Table A19 CS-DR column duplicates Table 6 5 km ATT but with a different SE (0.218 vs 0.210) — anomaly.\nAnomalies: (1) SE 0.210 (Table 6) vs 0.218 (Table A19) for same estimate; (2) 608 vs 640 control regions (A19 note); (3) abstract \"fatal fall by 35%\" vs Sec 6.1 \"~30% at 5 km\"; text says HIV \"close to zero\" though 5 km point is -0.340; (4) RESEA text gives weeks-on-UI CI as unsigned magnitudes; (5) \"p ¡ 0.001\" rendering defect. All recorded in results.json data_issues.\nInformation-availability: no input violates the rules; only the two extracts are used; README used for provenance/hash verification only. No exclusions applied (this is an extraction task; no rows filtered).\nEOF\ncat > out/exclusions.csv <<'EOF'\nstep,rule,n_before,n_after,n_removed,rationale\n0,none (extraction task; all 25 requested items assessed),25,25,0,No filtering applied; every requested item was located or explicitly reported as not printed\nEOF\necho \"- 2026-10-30 P1 passed: hashes verified, anomalies recorded. P2: baseline = direct as-printed extraction (no estimation involved); P3 analysis.py written and run.\" >> out/state/log.md\ncp out/results.json /tmp/r1.json && rm out/results.json && ~/wp29/venv/bin/python out/analysis.py >/dev/null && cmp out/results.json /tmp/r1.json && echo IDENTICAL\n~/wp29/venv/bin/python - <<'EOF'\nimport json,math\nr=json.load(open('out/results.json'))\nc=[]\nc.append((\"CI contains ATT\", r['dcr_att_5km_ci_low']<r['dcr_att_5km']<r['dcr_att_5km_ci_high']))\nc.append((\"CI half-width = 1.96*SE\", abs((r['dcr_att_5km_ci_high']-r['dcr_att_5km_ci_low'])/2-1.96*r['dcr_att_5km_se'])<1e-9))\nc.append((\"wage CI contains ATE\", r['ri_wage_ate_ci_low']<r['ri_wage_ate']<r['ri_wage_ate_ci_high']))\nc.append((\"weeks CI contains ATE & low<high\", r['ri_weeks_ui_ate_ci_low']<r['ri_weeks_ui_ate']<r['ri_weeks_ui_ate_ci_high']))\nc.append((\"SEs positive\", all(r[k]>0 for k in r if k.endswith('_se') or k=='ri_wage_ate_se_derived')))\nc.append((\"wage p=0.022 consistent with derived SE (z=1153/503.83)\", abs(2*(1-0.5*(1+math.erf(1153/503.83/math.sqrt(2))))-0.022)<0.002))\nc.append((\"HIV 5km no star & |t|<1.645\", (not r['dcr_hiv_att_5km_significant_10pct']) and abs(r['dcr_hiv_att_5km']/r['dcr_hiv_att_5km_se'])<1.645))\nc.append((\"5km ATT ** consistent: 1.96<|t|<2.576\", 1.96<abs(-0.431/0.210)<2.576))\nc.append((\"narcotics 3km *** consistent |t|>2.576\", abs(0.549/0.187)>2.576))\nc.append((\"fatal 1km *** consistent\", abs(1.207/0.137)>2.576))\nc.append((\"%change -35.0% = exp(-0.431)-1\", round(100*(math.exp(-0.431)-1),1)==-35.0))\nc.append((\"n_analysed = 25 and exclusions add up\", r['n_analysed']==25))\nc.append((\"all locators present\", all(k in r['locators'] for k in r if k.startswith(('dcr_','ri_')) and not isinstance(r[k],bool))))\nwith open('out/state/checks.md','w') as f:\n    f.write(\"# Verification checks (P4)\\n- Clean re-run of out/analysis.py: results.json byte-identical: PASS\\n\")\n    for n,ok in c: f.write(f\"- {n}: {'PASS' if ok else 'FAIL'}\\n\")\n    f.write(\"- Note: JSON cannot store trailing zeros; 0.210 is stored as 0.21, -0.340 as -0.34 (numerically identical to the printed values; printed strings appear in the script's stdout).\\n\")\nprint(open('out/state/checks.md').read())\nEOF","description":"Write audit, exclusions, rerun and consistency checks"},"caller":{"type":"direct"}}],"container":null,"stop_reason":"tool_use","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":693,"cache_read_input_tokens":37025,"output_tokens":2262,"output_tokens_details":{"thinking_tokens":0},"server_tool_use":{"web_search_requests":0,"web_fetch_requests":0},"service_tier":"standard","cache_creation":{"ephemeral_1h_input_tokens":0,"ephemeral_5m_input_tokens":693},"inference_geo":"global","iterations":[],"speed":"standard","fallback_credit":null},"input_transformations":[],"diagnostics":null,"context_management":null},"apiBlockIndex":1,"requestId":"req_011CgXsJx7tdK7KbRewF29tc","requestRef":"e394ee9b-aea9-417e-83c7-9818e55f6629","requestedModel":"claude-opus-5-5","type":"assistant","uuid":"75b28266-d2c0-4275-814e-d5d421c856b9","timestamp":"2026-10-30T05:27:46.959Z","effort":"medium","perTurnEffort":"medium","userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0288","sessionId":"6a59845e-54b1-4582-86f5-752e1f4df7e4","version":"2.1.314","gitBranch":"HEAD"}59{"parentUuid":"75b28266-d2c0-4275-814e-d5d421c856b9","isSidechain":false,"promptId":"214a8ece-2bbc-459c-aa1a-66062e2a4650","type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_011cR2P8Rx1guyFTnLCHpUgk","type":"tool_result","content":"IDENTICAL\n# Verification checks (P4)\n- Clean re-run of out/analysis.py: results.json byte-identical: PASS\n- CI contains ATT: PASS\n- CI half-width = 1.96*SE: PASS\n- wage CI contains ATE: PASS\n- weeks CI contains ATE & low<high: PASS\n- SEs positive: PASS\n- wage p=0.022 consistent with derived SE (z=1153/503.83): PASS\n- HIV 5km no star & |t|<1.645: PASS\n- 5km ATT ** consistent: 1.96<|t|<2.576: PASS\n- narcotics 3km *** consistent |t|>2.576: PASS\n- fatal 1km *** consistent: PASS\n- %change -35.0% = exp(-0.431)-1: PASS\n- n_analysed = 25 and exclusions add up: PASS\n- all locators present: PASS\n- Note: JSON cannot store trailing zeros; 0.210 is stored as 0.21, -0.340 as -0.34 (numerically identical to the printed values; printed strings appear in the script's stdout).","is_error":false}]},"uuid":"4476d3f0-0c9f-43c8-9cb1-9c39fca6ddb2","timestamp":"2026-10-30T05:27:47.059Z","toolUseResult":{"stdout":"IDENTICAL\n# Verification checks (P4)\n- Clean re-run of out/analysis.py: results.json byte-identical: PASS\n- CI contains ATT: PASS\n- CI half-width = 1.96*SE: PASS\n- wage CI contains ATE: PASS\n- weeks CI contains ATE & low<high: PASS\n- SEs positive: PASS\n- wage p=0.022 consistent with derived SE (z=1153/503.83): PASS\n- HIV 5km no star & |t|<1.645: PASS\n- 5km ATT ** consistent: 1.96<|t|<2.576: PASS\n- narcotics 3km *** consistent |t|>2.576: PASS\n- fatal 1km *** consistent: PASS\n- %change -35.0% = exp(-0.431)-1: PASS\n- n_analysed = 25 and exclusions add up: PASS\n- all locators present: PASS\n- Note: JSON cannot store trailing zeros; 0.210 is stored as 0.21, -0.340 as -0.34 (numerically identical to the printed values; printed strings appear in the script's stdout).","stderr":"","interrupted":false,"isImage":false,"noOutputExpected":false},"sandboxed":false,"sourceToolAssistantUUID":"75b28266-d2c0-4275-814e-d5d421c856b9","permissionDecision":{"decision":"accept","source":"config","reasonType":"rule","ruleSource":"cliArg"},"userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0288","sessionId":"6a59845e-54b1-4582-86f5-752e1f4df7e4","version":"2.1.314","gitBranch":"HEAD"}60{"type":"api-request-blob","sessionId":"6a59845e-54b1-4582-86f5-752e1f4df7e4","hash":"3253707e453d2fa71d7caa3d693ca4309648fa22333e2b325a1c03d26612db81","message":{"role":"assistant","content":[{"type":"text","text":"Now the P1/P2 artifacts, checks, and gate run."},{"type":"tool_use","id":"toolu_011cR2P8Rx1guyFTnLCHpUgk","name":"Bash","input":{"command":"cat > out/state/data_audit.md <<'EOF'\n# Data audit (P1)\n| file | SHA-256 | lines (wc -l) | matches README |\n|---|---|---|---|\n| data/README.md | 16ebdff389eeaee9508fe81c4edc43690f31f3463c469680c814134086ef9929 | 10 | (matches bundle.json) |\n| data/sources/arxiv-2609.27732v1.txt | ee2d444d2f78f2af2ca2cf69b59a61fcd7ac5e10ea29e8ddcd67917bf07d6a8b | 134 | yes |\n| data/sources/arxiv-2606.14621v1.txt | 27c463c0a144d9d5a3916dc641f15b6029f6c68b5e574ad9074e7df1aeb0a238 | 58 | yes |\n\nSchema: plain-text extracts; tables one row per line, \" | \" separators; DCR regression tables have SE line (parentheses) after each coefficient row. RESEA Tables 2–3 re-set one row per outcome with \"estimate; p=...; 95% CI: [lo, hi]\" cells.\nUnits: DCR ATTs in log points (outcome log hospitalisation rate per 100,000); Observations = region-years. RESEA wages annualised USD, reemployment % / pp, weeks on UI in weeks.\nDate ranges: DCR openings 1998–2022; RESEA RCT Feb 2022 – Sep 2023.\nMissing / duplicates: no missing cells in requested rows. Duplicates: RESEA Table 3 col 1 duplicates Table 2 ATE column (identical, checked). DCR Table A19 CS-DR column duplicates Table 6 5 km ATT but with a different SE (0.218 vs 0.210) — anomaly.\nAnomalies: (1) SE 0.210 (Table 6) vs 0.218 (Table A19) for same estimate; (2) 608 vs 640 control regions (A19 note); (3) abstract \"fatal fall by 35%\" vs Sec 6.1 \"~30% at 5 km\"; text says HIV \"close to zero\" though 5 km point is -0.340; (4) RESEA text gives weeks-on-UI CI as unsigned magnitudes; (5) \"p ¡ 0.001\" rendering defect. All recorded in results.json data_issues.\nInformation-availability: no input violates the rules; only the two extracts are used; README used for provenance/hash verification only. No exclusions applied (this is an extraction task; no rows filtered).\nEOF\ncat > out/exclusions.csv <<'EOF'\nstep,rule,n_before,n_after,n_removed,rationale\n0,none (extraction task; all 25 requested items assessed),25,25,0,No filtering applied; every requested item was located or explicitly reported as not printed\nEOF\necho \"- 2026-10-30 P1 passed: hashes verified, anomalies recorded. P2: baseline = direct as-printed extraction (no estimation involved); P3 analysis.py written and run.\" >> out/state/log.md\ncp out/results.json /tmp/r1.json && rm out/results.json && ~/wp29/venv/bin/python out/analysis.py >/dev/null && cmp out/results.json /tmp/r1.json && echo IDENTICAL\n~/wp29/venv/bin/python - <<'EOF'\nimport json,math\nr=json.load(open('out/results.json'))\nc=[]\nc.append((\"CI contains ATT\", r['dcr_att_5km_ci_low']<r['dcr_att_5km']<r['dcr_att_5km_ci_high']))\nc.append((\"CI half-width = 1.96*SE\", abs((r['dcr_att_5km_ci_high']-r['dcr_att_5km_ci_low'])/2-1.96*r['dcr_att_5km_se'])<1e-9))\nc.append((\"wage CI contains ATE\", r['ri_wage_ate_ci_low']<r['ri_wage_ate']<r['ri_wage_ate_ci_high']))\nc.append((\"weeks CI contains ATE & low<high\", r['ri_weeks_ui_ate_ci_low']<r['ri_weeks_ui_ate']<r['ri_weeks_ui_ate_ci_high']))\nc.append((\"SEs positive\", all(r[k]>0 for k in r if k.endswith('_se') or k=='ri_wage_ate_se_derived')))\nc.append((\"wage p=0.022 consistent with derived SE (z=1153/503.83)\", abs(2*(1-0.5*(1+math.erf(1153/503.83/math.sqrt(2))))-0.022)<0.002))\nc.append((\"HIV 5km no star & |t|<1.645\", (not r['dcr_hiv_att_5km_significant_10pct']) and abs(r['dcr_hiv_att_5km']/r['dcr_hiv_att_5km_se'])<1.645))\nc.append((\"5km ATT ** consistent: 1.96<|t|<2.576\", 1.96<abs(-0.431/0.210)<2.576))\nc.append((\"narcotics 3km *** consistent |t|>2.576\", abs(0.549/0.187)>2.576))\nc.append((\"fatal 1km *** consistent\", abs(1.207/0.137)>2.576))\nc.append((\"%change -35.0% = exp(-0.431)-1\", round(100*(math.exp(-0.431)-1),1)==-35.0))\nc.append((\"n_analysed = 25 and exclusions add up\", r['n_analysed']==25))\nc.append((\"all locators present\", all(k in r['locators'] for k in r if k.startswith(('dcr_','ri_')) and not isinstance(r[k],bool))))\nwith open('out/state/checks.md','w') as f:\n    f.write(\"# Verification checks (P4)\\n- Clean re-run of out/analysis.py: results.json byte-identical: PASS\\n\")\n    for n,ok in c: f.write(f\"- {n}: {'PASS' if ok else 'FAIL'}\\n\")\n    f.write(\"- Note: JSON cannot store trailing zeros; 0.210 is stored as 0.21, -0.340 as -0.34 (numerically identical to the printed values; printed strings appear in the script's stdout).\\n\")\nprint(open('out/state/checks.md').read())\nEOF","description":"Write audit, exclusions, rerun and consistency checks"}}]}}61{"type":"api-request","sessionId":"6a59845e-54b1-4582-86f5-752e1f4df7e4","id":"fce9a7bf-1391-482a-9edf-8ba2649b4467","timestamp":"2026-10-30T05:27:47.062Z","version":"2.1.314","querySource":"sdk","shapeHash":"48ce5b29cfebf0ef61214c91a04304198dbc6c218e84d6eb22b1863e5edbbfe3","params":{"model":"claude-opus-5-5","betas":["claude-code-20250219","interleaved-thinking-2025-05-14","context-management-2025-06-27","prompt-caching-scope-2026-01-05","per-turn-control-2026-07-01","thinking-token-count-2026-05-13","mid-conversation-system-clear-at-2026-08-21","effort-2025-11-24","thinking-binding-controls-2026-08-01","thinking-display-updates-2026-08-18","cache-diagnosis-2026-04-07"],"max_tokens":128000,"thinking":{"type":"adaptive","display":"updates"},"context_management":{"edits":[{"type":"clear_thinking_20251015","keep":"all"}]},"output_config":{"effort":"medium"}},"base":"e394ee9b-aea9-417e-83c7-9818e55f6629","keep":16,"tail":["3253707e453d2fa71d7caa3d693ca4309648fa22333e2b325a1c03d26612db81","3e11569a343d6179be9efd868f4ca37ffb1056d94475fa2b02e74fc07d360f1a"],"cacheControl":[{"path":["system",0],"value":{"type":"ephemeral"}},{"path":["system",1],"value":{"type":"ephemeral"}},{"path":["messages",17,"content",0],"value":{"type":"ephemeral"}}],"requestDigest":"dde4fdad6c69c72c25b67ae793bdda6d9095f2ec111f384ed1605bb39185d58b"}62{"parentUuid":"4476d3f0-0c9f-43c8-9cb1-9c39fca6ddb2","isSidechain":false,"message":{"model":"claude-opus-5-5","id":"msg_011CgXsLSA91rXez3LYyXbtH","type":"message","role":"assistant","content":[{"type":"thinking","thinking":"","signature":"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"}],"container":null,"stop_reason":"tool_use","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":2643,"cache_read_input_tokens":37718,"output_tokens":4200,"output_tokens_details":{"thinking_tokens":30},"server_tool_use":{"web_search_requests":0,"web_fetch_requests":0},"service_tier":"standard","cache_creation":{"ephemeral_1h_input_tokens":0,"ephemeral_5m_input_tokens":2643},"inference_geo":"global","iterations":[],"speed":"standard","fallback_credit":null},"input_transformations":[],"diagnostics":null,"context_management":null},"thinkingDurationMs":707,"thinkingDisplay":"updates","apiBlockIndex":0,"requestId":"req_011CgXsLRv17fxhw7uCP3Mbd","requestRef":"fce9a7bf-1391-482a-9edf-8ba2649b4467","requestedModel":"claude-opus-5-5","type":"assistant","uuid":"9695ac9b-960a-4787-bfea-e61d7b5925af","timestamp":"2026-10-30T05:27:49.123Z","effort":"medium","perTurnEffort":"medium","userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0288","sessionId":"6a59845e-54b1-4582-86f5-752e1f4df7e4","version":"2.1.314","gitBranch":"HEAD"}63{"parentUuid":"9695ac9b-960a-4787-bfea-e61d7b5925af","isSidechain":false,"message":{"model":"claude-opus-5-5","id":"msg_011CgXsLSA91rXez3LYyXbtH","type":"message","role":"assistant","content":[{"type":"tool_use","id":"toolu_01Kc2qCW5mkRsZzSDfFZ1Q6b","name":"Bash","input":{"command":"cat > out/report.md <<'EOF'\n# F4-conf-01 — Treatment effects and uncertainty extracted from two published tables\n\n## Question\nExtract, exactly as printed, specified point estimates, standard errors (SEs), 95% confidence-interval (CI) bounds and counts from (i) Armendariz (2026), arXiv:2609.27732v1 (staggered DiD of drug consumption room openings, Tables 6 and 7) and (ii) Li, Pearson-Merkowitz & Yokum (2026), arXiv:2606.14621v1 (RESEA RCT, Table 2). Each value gets a locator. I also derive a 95% CI from a printed SE and an SE from a printed CI, and I flag any requested quantity that the source does not print. Nothing is re-estimated.\n\n## Data and exclusions\nThe only inputs were the two source extracts and `data/README.md`. Line counts are in `n_input_rows`: 134, 58 and 10 lines. The SHA-256 of each extract matches the README. No rows were excluded (`out/exclusions.csv`). All 25 requested items were assessed (`n_analysed` = 25). Six input issues are recorded in `data_issues`. The most relevant one: the same 5 km Callaway–Sant'Anna ATT of -0.431 has SE 0.210 in Table 6 but SE 0.218 in Appendix Table A19. Following the key definitions, I used Table 6. The other issues are:\n- the number of control regions is 608 in some places and 640 in the A19 note;\n- the abstract gives a 35% fall in fatal hospitalisations, while the body text gives about 30%;\n- the RESEA text gives the weeks-on-UI CI as unsigned magnitudes, while the table gives signed values;\n- the text has a \"p ¡ 0.001\" rendering defect;\n- the extract editor re-set RESEA Table 2.\n\nNone of these issues changes any extracted value.\n\n## Method\n`out/analysis.py` parses the pipe-separated table lines, finds cells by row and column label, and prints each matched source line. It strips stars, `$`, thousands separators and parentheses, and keeps the sign. Values printed without decimals are stored as integers. JSON does not keep trailing zeros, so 0.210 is stored as 0.21 and -0.340 as -0.34. These are numerically identical, and the printed strings appear in the script's output. For the derived quantities:\n- **CI from SE:** ATT ± 1.96 × SE (`derivation_notes.dcr_att_5km_ci`).\n- **SE from CI:** (upper − lower) / (2 × 1.96) (`derivation_notes.ri_wage_ate_se`).\n\nNo randomness is involved (`random_seed` = null).\n\n## Results\n**DCR study (log points)**\n\n| Item | Table | Value | SE |\n|---|---|---|---|\n| ATT, 5 km | 6 | `dcr_att_5km` = -0.431 (\\*\\*) | `dcr_att_5km_se` = 0.210 |\n| ATT, 3 km | 6 | `dcr_att_3km` = -0.283 | `dcr_att_3km_se` = 0.279 |\n| Narcotics ATT, 3 km | 7 | `dcr_narcotics_att_3km` = 0.549 (\\*\\*\\*) | `dcr_narcotics_att_3km_se` = 0.187 |\n| Fatal drug-related ATT, 1 km | 7 | `dcr_fatal_att_1km` = -1.207 (\\*\\*\\*) | `dcr_fatal_att_1km_se` = 0.137 |\n| HIV ATT, 5 km | 7 | `dcr_hiv_att_5km` = -0.340 | `dcr_hiv_att_5km_se` = 0.294 |\n\n- Observations at 5 km: `dcr_obs_5km` = 17250 region-years (Table 6).\n- The HIV 5 km ATT has no star, so it is not marked significant at 10% (`dcr_hiv_att_5km_significant_10pct` = false).\n- Its exact p-value is **not reported** in the extract, which prints only stars (`dcr_hiv_att_5km_p_value` = null).\n- Derived 95% CI for the Table 6 5 km ATT: [`dcr_att_5km_ci_low` = -0.8426, `dcr_att_5km_ci_high` = -0.0194].\n\n**RESEA study, Table 2**\n- Wage ATE: `ri_wage_ate` = 1153 USD/yr, with printed 95% CI [`ri_wage_ate_ci_low` = 166, `ri_wage_ate_ci_high` = 2141].\n- No SE is printed for the wage ATE (`ri_wage_ate_se_reported` = false). The SE derived from the CI is `ri_wage_ate_se_derived` = 503.83 USD/yr.\n- Control-group mean wage: `ri_control_mean_wage` = 33043 USD/yr.\n- Reemployment ATE: `ri_reemployment_ate_pp` = 1.47 pp.\n- Weeks-on-UI ATE: `ri_weeks_ui_ate` = -1.99 weeks, with CI [`ri_weeks_ui_ate_ci_low` = -2.24, `ri_weeks_ui_ate_ci_high` = -1.75].\n\n## Uncertainty\n- **DCR (Tables 6 and 7):** uncertainty is printed as **standard errors** clustered at the MedStat region level, in parentheses. Significance stars mark p<0.01, p<0.05 and p<0.10. No CIs and no exact p-values are printed for the overall ATTs.\n- **RESEA (Table 2):** uncertainty is printed as **95% confidence intervals** with two-sided p-values. No standard errors are printed.\n- **What the derived quantities assume:** both assume an approximately normal (Wald) estimator with critical value 1.96. The SE derivation also assumes the printed CI is symmetric. It is nearly so: 987 below and 988 above 1153. With N = 23,549, a t critical value would be practically identical.\n- **Check on the wage SE:** the implied z = 1153/503.83 ≈ 2.29 gives p ≈ 0.022, matching the printed p (`out/state/checks.md`).\n- **Sensitivity of the DCR CI:** the derived CI depends on using the Table 6 SE. With the A19 SE of 0.218 the interval would be wider. That alternative is not reported as a result.\n\n## Limitations and open issues\n- I could not produce an exact p-value for the HIV 5 km ATT (null). Computing one would need a distributional assumption, and the task asks for the printed value.\n- The two DCR tables disagree on the 5 km ATT SE (0.210 vs 0.218). The extract does not explain why; I used Table 6 as instructed.\n- The derived CI and SE are approximations under the conventions stated above, not values from the sources.\n- The RESEA Table 2 layout was re-set by the extract editor. I relied on the editor saying the values are verbatim. As a cross-check, Table 3 repeats the wage ATE cell identically.\n- I judged the task complete (`feasibility` = \"complete\"). The null p-value is the expected answer for a quantity the source does not report, not a failure.\nEOF\ncat > out/claims.json <<'EOF'\n[\n {\"id\":\"C1\",\"text\":\"Table 6 prints a 5 km ATT of -0.431 (**) with clustered SE 0.210, 3 km ATT -0.283 (SE 0.279), and 17,250 observations at 5 km.\",\"results_keys\":[\"dcr_att_5km\",\"dcr_att_5km_se\",\"dcr_att_3km\",\"dcr_att_3km_se\",\"dcr_obs_5km\"],\"status\":\"supported\",\"caveat\":\"Appendix Table A19 prints SE 0.218 for the same estimate; Table 6 used as required.\"},\n {\"id\":\"C2\",\"text\":\"Table 7 prints narcotics 3 km ATT 0.549 (SE 0.187), fatal 1 km ATT -1.207 (SE 0.137), HIV 5 km ATT -0.340 (SE 0.294).\",\"results_keys\":[\"dcr_narcotics_att_3km\",\"dcr_narcotics_att_3km_se\",\"dcr_fatal_att_1km\",\"dcr_fatal_att_1km_se\",\"dcr_hiv_att_5km\",\"dcr_hiv_att_5km_se\"],\"status\":\"supported\",\"caveat\":\"JSON drops trailing zeros (-0.34 = -0.340).\"},\n {\"id\":\"C3\",\"text\":\"The HIV 5 km ATT carries no significance star (not significant at 10%) and its exact p-value is not reported in the extract.\",\"results_keys\":[\"dcr_hiv_att_5km_significant_10pct\",\"dcr_hiv_att_5km_p_value\"],\"status\":\"supported\",\"caveat\":\"p-value left null rather than computed under a normality assumption.\"},\n {\"id\":\"C4\",\"text\":\"Derived 95% CI for the Table 6 5 km ATT under a normal 1.96 convention is [-0.8426, -0.0194].\",\"results_keys\":[\"dcr_att_5km_ci_low\",\"dcr_att_5km_ci_high\",\"derivation_notes\"],\"status\":\"supported\",\"caveat\":\"Derived, not printed; assumes asymptotic normality and the Table 6 SE.\"},\n {\"id\":\"C5\",\"text\":\"RESEA Table 2 prints a wage ATE of $1,153 with 95% CI [166, 2,141], a control mean wage of $33,043, a reemployment ATE of 1.47 pp, and a weeks-on-UI ATE of -1.99 with CI [-2.24, -1.75].\",\"results_keys\":[\"ri_wage_ate\",\"ri_wage_ate_ci_low\",\"ri_wage_ate_ci_high\",\"ri_control_mean_wage\",\"ri_reemployment_ate_pp\",\"ri_weeks_ui_ate\",\"ri_weeks_ui_ate_ci_low\",\"ri_weeks_ui_ate_ci_high\"],\"status\":\"supported\",\"caveat\":\"Table 2 layout was re-set by the extract editor.\"},\n {\"id\":\"C6\",\"text\":\"No SE is printed for the wage ATE; the SE implied by the printed 95% CI with critical value 1.96 is 503.83 USD/yr.\",\"results_keys\":[\"ri_wage_ate_se_reported\",\"ri_wage_ate_se_derived\",\"derivation_notes\"],\"status\":\"supported\",\"caveat\":\"Assumes a symmetric Wald interval; consistent with the printed p=0.022.\"},\n {\"id\":\"C7\",\"text\":\"The inputs contain internal inconsistencies (Table 6 vs A19 SE; 608 vs 640 controls; abstract vs text percentages; unsigned CI in text; rendering defect) that do not change any extracted value.\",\"results_keys\":[\"data_issues\"],\"status\":\"supported\",\"caveat\":\"Inconsistencies flagged, not resolved.\"}\n]\nEOF\n~/wp29/venv/bin/python - <<'EOF'\nimport json\np=json.load(open('out/state/phases.json'))\np['P1']={\"status\":\"passed\",\"checks\":[\"SHA-256 of all inputs computed and matched README\",\"line counts recorded\",\"anomalies listed in data_audit.md\",\"exclusions.csv written (0 removed)\"],\"notes\":\"\"}\np['P2']={\"status\":\"passed\",\"checks\":[\"baseline = direct as-printed extraction of required keys written to results.json\"],\"notes\":\"No estimation; the printed values are the estimands.\"}\np['P3']={\"status\":\"passed\",\"checks\":[\"analysis.py parses data/ only, prints matched lines, writes results.json; no randomness\"],\"notes\":\"\"}\np['P4']={\"status\":\"running\",\"checks\":[\"clean re-run byte-identical\",\"13 consistency checks PASS (checks.md)\"],\"notes\":\"\"}\njson.dump(p,open('out/state/phases.json','w'),indent=1)\nEOF\n~/wp29/venv/bin/python ~/wp29/gates/pgar_gate.py .; ls out/state/gate_receipts","description":"Write report, claims, update phases, run gate"},"caller":{"type":"direct"}}],"container":null,"stop_reason":"tool_use","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":2643,"cache_read_input_tokens":37718,"output_tokens":4200,"output_tokens_details":{"thinking_tokens":30},"server_tool_use":{"web_search_requests":0,"web_fetch_requests":0},"service_tier":"standard","cache_creation":{"ephemeral_1h_input_tokens":0,"ephemeral_5m_input_tokens":2643},"inference_geo":"global","iterations":[],"speed":"standard","fallback_credit":null},"input_transformations":[],"diagnostics":null,"context_management":null},"apiBlockIndex":1,"requestId":"req_011CgXsLRv17fxhw7uCP3Mbd","requestRef":"fce9a7bf-1391-482a-9edf-8ba2649b4467","requestedModel":"claude-opus-5-5","type":"assistant","uuid":"f5dd1cff-7401-4fe4-bbb5-08ecb3c2ed2a","timestamp":"2026-10-30T05:28:20.948Z","effort":"medium","perTurnEffort":"medium","userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0288","sessionId":"6a59845e-54b1-4582-86f5-752e1f4df7e4","version":"2.1.314","gitBranch":"HEAD"}64{"parentUuid":"f5dd1cff-7401-4fe4-bbb5-08ecb3c2ed2a","isSidechain":false,"promptId":"214a8ece-2bbc-459c-aa1a-66062e2a4650","type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_01Kc2qCW5mkRsZzSDfFZ1Q6b","type":"tool_result","content":"PGAR GATE pgar_gate/1.0 — FAILED (1 of 48 checks failed)\n  [ok] file:out/results.json: present\n  [ok] file:out/report.md: present\n  [ok] file:out/analysis.py: present\n  [ok] file:out/claims.json: present\n  [ok] results_json: parsed\n  [ok] key:dcr_att_5km: ok\n  [ok] key:dcr_att_5km_se: ok\n  [ok] key:dcr_att_3km: ok\n  [ok] key:dcr_att_3km_se: ok\n  [ok] key:dcr_obs_5km: ok\n  [ok] key:dcr_narcotics_att_3km: ok\n  [ok] key:dcr_narcotics_att_3km_se: ok\n  [ok] key:dcr_fatal_att_1km: ok\n  [ok] key:dcr_fatal_att_1km_se: ok\n  [ok] key:dcr_hiv_att_5km: ok\n  [ok] key:dcr_hiv_att_5km_se: ok\n  [ok] key:ri_wage_ate: ok\n  [ok] key:ri_wage_ate_ci_low: ok\n  [ok] key:ri_wage_ate_ci_high: ok\n  [ok] key:ri_control_mean_wage: ok\n  [ok] key:ri_reemployment_ate_pp: ok\n  [ok] key:ri_weeks_ui_ate: ok\n  [ok] key:ri_weeks_ui_ate_ci_low: ok\n  [ok] key:ri_weeks_ui_ate_ci_high: ok\n  [ok] key:dcr_att_5km_ci_low: ok\n  [ok] key:dcr_att_5km_ci_high: ok\n  [ok] key:ri_wage_ate_se_derived: ok\n  [ok] key:dcr_hiv_att_5km_p_value: ok\n  [ok] key:dcr_hiv_att_5km_significant_10pct: ok\n  [ok] key:ri_wage_ate_se_reported: ok\n  [ok] key:locators: ok\n  [ok] key:derivation_notes: ok\n  [ok] key:task_id: ok\n  [ok] key:feasibility: ok\n  [ok] key:feasibility_reason: ok\n  [ok] key:data_issues: ok\n  [ok] key:n_input_rows: ok\n  [ok] key:n_analysed: ok\n  [ok] key:random_seed: ok\n  [ok] section:Question: present\n  [ok] section:Data and exclusions: present\n  [ok] section:Method: present\n  [ok] section:Results: present\n  [ok] section:Uncertainty: present\n  [ok] section:Limitations and open issues: present\n  [ok] claims_json: 7 claims, all keys resolve\n  [FAIL] phase_ledger: phases not terminal: ['P4', 'P5']\n  [ok] reproducibility: regenerated in 0.0s; identical within tolerance\nreceipt: /Users/simon-pierreboucher/wp29/runs/RC0288/out/state/gate_receipts/20261030T052821.064096Z.json\n20261030T052821.064096Z.json","is_error":false}]},"uuid":"da5e30ed-470d-47ec-b6b9-73b597268e1f","timestamp":"2026-10-30T05:28:21.071Z","toolUseResult":{"stdout":"PGAR GATE pgar_gate/1.0 — FAILED (1 of 48 checks failed)\n  [ok] file:out/results.json: present\n  [ok] file:out/report.md: present\n  [ok] file:out/analysis.py: present\n  [ok] file:out/claims.json: present\n  [ok] results_json: parsed\n  [ok] key:dcr_att_5km: ok\n  [ok] key:dcr_att_5km_se: ok\n  [ok] key:dcr_att_3km: ok\n  [ok] key:dcr_att_3km_se: ok\n  [ok] key:dcr_obs_5km: ok\n  [ok] key:dcr_narcotics_att_3km: ok\n  [ok] key:dcr_narcotics_att_3km_se: ok\n  [ok] key:dcr_fatal_att_1km: ok\n  [ok] key:dcr_fatal_att_1km_se: ok\n  [ok] key:dcr_hiv_att_5km: ok\n  [ok] key:dcr_hiv_att_5km_se: ok\n  [ok] key:ri_wage_ate: ok\n  [ok] key:ri_wage_ate_ci_low: ok\n  [ok] key:ri_wage_ate_ci_high: ok\n  [ok] key:ri_control_mean_wage: ok\n  [ok] key:ri_reemployment_ate_pp: ok\n  [ok] key:ri_weeks_ui_ate: ok\n  [ok] key:ri_weeks_ui_ate_ci_low: ok\n  [ok] key:ri_weeks_ui_ate_ci_high: ok\n  [ok] key:dcr_att_5km_ci_low: ok\n  [ok] key:dcr_att_5km_ci_high: ok\n  [ok] key:ri_wage_ate_se_derived: ok\n  [ok] key:dcr_hiv_att_5km_p_value: ok\n  [ok] key:dcr_hiv_att_5km_significant_10pct: ok\n  [ok] key:ri_wage_ate_se_reported: ok\n  [ok] key:locators: ok\n  [ok] key:derivation_notes: ok\n  [ok] key:task_id: ok\n  [ok] key:feasibility: ok\n  [ok] key:feasibility_reason: ok\n  [ok] key:data_issues: ok\n  [ok] key:n_input_rows: ok\n  [ok] key:n_analysed: ok\n  [ok] key:random_seed: ok\n  [ok] section:Question: present\n  [ok] section:Data and exclusions: present\n  [ok] section:Method: present\n  [ok] section:Results: present\n  [ok] section:Uncertainty: present\n  [ok] section:Limitations and open issues: present\n  [ok] claims_json: 7 claims, all keys resolve\n  [FAIL] phase_ledger: phases not terminal: ['P4', 'P5']\n  [ok] reproducibility: regenerated in 0.0s; identical within tolerance\nreceipt: /Users/simon-pierreboucher/wp29/runs/RC0288/out/state/gate_receipts/20261030T052821.064096Z.json\n20261030T052821.064096Z.json","stderr":"","interrupted":false,"isImage":false,"noOutputExpected":false},"sandboxed":false,"sourceToolAssistantUUID":"f5dd1cff-7401-4fe4-bbb5-08ecb3c2ed2a","permissionDecision":{"decision":"accept","source":"config","reasonType":"rule","ruleSource":"cliArg"},"userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0288","sessionId":"6a59845e-54b1-4582-86f5-752e1f4df7e4","version":"2.1.314","gitBranch":"HEAD"}65{"type":"last-prompt","lastPrompt":"Begin","leafUuid":"da5e30ed-470d-47ec-b6b9-73b597268e1f","sessionId":"6a59845e-54b1-4582-86f5-752e1f4df7e4"}66{"type":"atis-latch","atis":"","sessionId":"6a59845e-54b1-4582-86f5-752e1f4df7e4"}67{"type":"api-request-blob","sessionId":"6a59845e-54b1-4582-86f5-752e1f4df7e4","hash":"991af66a9c10308efb60f5a01c54c881f48abadff1eb376c65304c3c99e946f6","message":{"role":"assistant","content":[{"type":"thinking","thinking":"","signature":"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"},{"type":"tool_use","id":"toolu_01Kc2qCW5mkRsZzSDfFZ1Q6b","name":"Bash","input":{"command":"cat > out/report.md <<'EOF'\n# F4-conf-01 — Treatment effects and uncertainty extracted from two published tables\n\n## Question\nExtract, exactly as printed, specified point estimates, standard errors (SEs), 95% confidence-interval (CI) bounds and counts from (i) Armendariz (2026), arXiv:2609.27732v1 (staggered DiD of drug consumption room openings, Tables 6 and 7) and (ii) Li, Pearson-Merkowitz & Yokum (2026), arXiv:2606.14621v1 (RESEA RCT, Table 2). Each value gets a locator. I also derive a 95% CI from a printed SE and an SE from a printed CI, and I flag any requested quantity that the source does not print. Nothing is re-estimated.\n\n## Data and exclusions\nThe only inputs were the two source extracts and `data/README.md`. Line counts are in `n_input_rows`: 134, 58 and 10 lines. The SHA-256 of each extract matches the README. No rows were excluded (`out/exclusions.csv`). All 25 requested items were assessed (`n_analysed` = 25). Six input issues are recorded in `data_issues`. The most relevant one: the same 5 km Callaway–Sant'Anna ATT of -0.431 has SE 0.210 in Table 6 but SE 0.218 in Appendix Table A19. Following the key definitions, I used Table 6. The other issues are:\n- the number of control regions is 608 in some places and 640 in the A19 note;\n- the abstract gives a 35% fall in fatal hospitalisations, while the body text gives about 30%;\n- the RESEA text gives the weeks-on-UI CI as unsigned magnitudes, while the table gives signed values;\n- the text has a \"p ¡ 0.001\" rendering defect;\n- the extract editor re-set RESEA Table 2.\n\nNone of these issues changes any extracted value.\n\n## Method\n`out/analysis.py` parses the pipe-separated table lines, finds cells by row and column label, and prints each matched source line. It strips stars, `$`, thousands separators and parentheses, and keeps the sign. Values printed without decimals are stored as integers. JSON does not keep trailing zeros, so 0.210 is stored as 0.21 and -0.340 as -0.34. These are numerically identical, and the printed strings appear in the script's output. For the derived quantities:\n- **CI from SE:** ATT ± 1.96 × SE (`derivation_notes.dcr_att_5km_ci`).\n- **SE from CI:** (upper − lower) / (2 × 1.96) (`derivation_notes.ri_wage_ate_se`).\n\nNo randomness is involved (`random_seed` = null).\n\n## Results\n**DCR study (log points)**\n\n| Item | Table | Value | SE |\n|---|---|---|---|\n| ATT, 5 km | 6 | `dcr_att_5km` = -0.431 (\\*\\*) | `dcr_att_5km_se` = 0.210 |\n| ATT, 3 km | 6 | `dcr_att_3km` = -0.283 | `dcr_att_3km_se` = 0.279 |\n| Narcotics ATT, 3 km | 7 | `dcr_narcotics_att_3km` = 0.549 (\\*\\*\\*) | `dcr_narcotics_att_3km_se` = 0.187 |\n| Fatal drug-related ATT, 1 km | 7 | `dcr_fatal_att_1km` = -1.207 (\\*\\*\\*) | `dcr_fatal_att_1km_se` = 0.137 |\n| HIV ATT, 5 km | 7 | `dcr_hiv_att_5km` = -0.340 | `dcr_hiv_att_5km_se` = 0.294 |\n\n- Observations at 5 km: `dcr_obs_5km` = 17250 region-years (Table 6).\n- The HIV 5 km ATT has no star, so it is not marked significant at 10% (`dcr_hiv_att_5km_significant_10pct` = false).\n- Its exact p-value is **not reported** in the extract, which prints only stars (`dcr_hiv_att_5km_p_value` = null).\n- Derived 95% CI for the Table 6 5 km ATT: [`dcr_att_5km_ci_low` = -0.8426, `dcr_att_5km_ci_high` = -0.0194].\n\n**RESEA study, Table 2**\n- Wage ATE: `ri_wage_ate` = 1153 USD/yr, with printed 95% CI [`ri_wage_ate_ci_low` = 166, `ri_wage_ate_ci_high` = 2141].\n- No SE is printed for the wage ATE (`ri_wage_ate_se_reported` = false). The SE derived from the CI is `ri_wage_ate_se_derived` = 503.83 USD/yr.\n- Control-group mean wage: `ri_control_mean_wage` = 33043 USD/yr.\n- Reemployment ATE: `ri_reemployment_ate_pp` = 1.47 pp.\n- Weeks-on-UI ATE: `ri_weeks_ui_ate` = -1.99 weeks, with CI [`ri_weeks_ui_ate_ci_low` = -2.24, `ri_weeks_ui_ate_ci_high` = -1.75].\n\n## Uncertainty\n- **DCR (Tables 6 and 7):** uncertainty is printed as **standard errors** clustered at the MedStat region level, in parentheses. Significance stars mark p<0.01, p<0.05 and p<0.10. No CIs and no exact p-values are printed for the overall ATTs.\n- **RESEA (Table 2):** uncertainty is printed as **95% confidence intervals** with two-sided p-values. No standard errors are printed.\n- **What the derived quantities assume:** both assume an approximately normal (Wald) estimator with critical value 1.96. The SE derivation also assumes the printed CI is symmetric. It is nearly so: 987 below and 988 above 1153. With N = 23,549, a t critical value would be practically identical.\n- **Check on the wage SE:** the implied z = 1153/503.83 ≈ 2.29 gives p ≈ 0.022, matching the printed p (`out/state/checks.md`).\n- **Sensitivity of the DCR CI:** the derived CI depends on using the Table 6 SE. With the A19 SE of 0.218 the interval would be wider. That alternative is not reported as a result.\n\n## Limitations and open issues\n- I could not produce an exact p-value for the HIV 5 km ATT (null). Computing one would need a distributional assumption, and the task asks for the printed value.\n- The two DCR tables disagree on the 5 km ATT SE (0.210 vs 0.218). The extract does not explain why; I used Table 6 as instructed.\n- The derived CI and SE are approximations under the conventions stated above, not values from the sources.\n- The RESEA Table 2 layout was re-set by the extract editor. I relied on the editor saying the values are verbatim. As a cross-check, Table 3 repeats the wage ATE cell identically.\n- I judged the task complete (`feasibility` = \"complete\"). The null p-value is the expected answer for a quantity the source does not report, not a failure.\nEOF\ncat > out/claims.json <<'EOF'\n[\n {\"id\":\"C1\",\"text\":\"Table 6 prints a 5 km ATT of -0.431 (**) with clustered SE 0.210, 3 km ATT -0.283 (SE 0.279), and 17,250 observations at 5 km.\",\"results_keys\":[\"dcr_att_5km\",\"dcr_att_5km_se\",\"dcr_att_3km\",\"dcr_att_3km_se\",\"dcr_obs_5km\"],\"status\":\"supported\",\"caveat\":\"Appendix Table A19 prints SE 0.218 for the same estimate; Table 6 used as required.\"},\n {\"id\":\"C2\",\"text\":\"Table 7 prints narcotics 3 km ATT 0.549 (SE 0.187), fatal 1 km ATT -1.207 (SE 0.137), HIV 5 km ATT -0.340 (SE 0.294).\",\"results_keys\":[\"dcr_narcotics_att_3km\",\"dcr_narcotics_att_3km_se\",\"dcr_fatal_att_1km\",\"dcr_fatal_att_1km_se\",\"dcr_hiv_att_5km\",\"dcr_hiv_att_5km_se\"],\"status\":\"supported\",\"caveat\":\"JSON drops trailing zeros (-0.34 = -0.340).\"},\n {\"id\":\"C3\",\"text\":\"The HIV 5 km ATT carries no significance star (not significant at 10%) and its exact p-value is not reported in the extract.\",\"results_keys\":[\"dcr_hiv_att_5km_significant_10pct\",\"dcr_hiv_att_5km_p_value\"],\"status\":\"supported\",\"caveat\":\"p-value left null rather than computed under a normality assumption.\"},\n {\"id\":\"C4\",\"text\":\"Derived 95% CI for the Table 6 5 km ATT under a normal 1.96 convention is [-0.8426, -0.0194].\",\"results_keys\":[\"dcr_att_5km_ci_low\",\"dcr_att_5km_ci_high\",\"derivation_notes\"],\"status\":\"supported\",\"caveat\":\"Derived, not printed; assumes asymptotic normality and the Table 6 SE.\"},\n {\"id\":\"C5\",\"text\":\"RESEA Table 2 prints a wage ATE of $1,153 with 95% CI [166, 2,141], a control mean wage of $33,043, a reemployment ATE of 1.47 pp, and a weeks-on-UI ATE of -1.99 with CI [-2.24, -1.75].\",\"results_keys\":[\"ri_wage_ate\",\"ri_wage_ate_ci_low\",\"ri_wage_ate_ci_high\",\"ri_control_mean_wage\",\"ri_reemployment_ate_pp\",\"ri_weeks_ui_ate\",\"ri_weeks_ui_ate_ci_low\",\"ri_weeks_ui_ate_ci_high\"],\"status\":\"supported\",\"caveat\":\"Table 2 layout was re-set by the extract editor.\"},\n {\"id\":\"C6\",\"text\":\"No SE is printed for the wage ATE; the SE implied by the printed 95% CI with critical value 1.96 is 503.83 USD/yr.\",\"results_keys\":[\"ri_wage_ate_se_reported\",\"ri_wage_ate_se_derived\",\"derivation_notes\"],\"status\":\"supported\",\"caveat\":\"Assumes a symmetric Wald interval; consistent with the printed p=0.022.\"},\n {\"id\":\"C7\",\"text\":\"The inputs contain internal inconsistencies (Table 6 vs A19 SE; 608 vs 640 controls; abstract vs text percentages; unsigned CI in text; rendering defect) that do not change any extracted value.\",\"results_keys\":[\"data_issues\"],\"status\":\"supported\",\"caveat\":\"Inconsistencies flagged, not resolved.\"}\n]\nEOF\n~/wp29/venv/bin/python - <<'EOF'\nimport json\np=json.load(open('out/state/phases.json'))\np['P1']={\"status\":\"passed\",\"checks\":[\"SHA-256 of all inputs computed and matched README\",\"line counts recorded\",\"anomalies listed in data_audit.md\",\"exclusions.csv written (0 removed)\"],\"notes\":\"\"}\np['P2']={\"status\":\"passed\",\"checks\":[\"baseline = direct as-printed extraction of required keys written to results.json\"],\"notes\":\"No estimation; the printed values are the estimands.\"}\np['P3']={\"status\":\"passed\",\"checks\":[\"analysis.py parses data/ only, prints matched lines, writes results.json; no randomness\"],\"notes\":\"\"}\np['P4']={\"status\":\"running\",\"checks\":[\"clean re-run byte-identical\",\"13 consistency checks PASS (checks.md)\"],\"notes\":\"\"}\njson.dump(p,open('out/state/phases.json','w'),indent=1)\nEOF\n~/wp29/venv/bin/python ~/wp29/gates/pgar_gate.py .; 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Next I'll record the receipt and finalise the ledger."}],"container":null,"stop_reason":"tool_use","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":5287,"cache_read_input_tokens":40361,"output_tokens":575,"output_tokens_details":{"thinking_tokens":44},"server_tool_use":{"web_search_requests":0,"web_fetch_requests":0},"service_tier":"standard","cache_creation":{"ephemeral_1h_input_tokens":0,"ephemeral_5m_input_tokens":5287},"inference_geo":"global","iterations":[],"speed":"standard","fallback_credit":null},"input_transformations":[],"diagnostics":null,"context_management":null},"apiBlockIndex":1,"requestId":"req_011CgXsNwLd1LvDi1RrkjkiR","requestRef":"ed62dba7-de53-4589-aa4d-bf60cccebf11","requestedModel":"claude-opus-5-5","type":"assistant","uuid":"9bef9238-de78-4996-9925-b81fa1b10544","timestamp":"2026-10-30T05:28:23.167Z","effort":"medium","perTurnEffort":"medium","userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0288","sessionId":"6a59845e-54b1-4582-86f5-752e1f4df7e4","version":"2.1.314","gitBranch":"HEAD"}71{"parentUuid":"9bef9238-de78-4996-9925-b81fa1b10544","isSidechain":false,"message":{"model":"claude-opus-5-5","id":"msg_011CgXsNx2ogf6YBt76oqjKD","type":"message","role":"assistant","content":[{"type":"tool_use","id":"toolu_01CKhwdi2JBaApk9FGjvDxAL","name":"Bash","input":{"command":"~/wp29/venv/bin/python - <<'EOF'\nimport json\np=json.load(open('out/state/phases.json'))\np['P4']={\"status\":\"passed\",\"checks\":[\"clean re-run byte-identical\",\"13 consistency checks PASS (checks.md)\",\"gate run: 47/48 ok; only failure = P4/P5 not yet terminal (expected mid-run)\"],\"notes\":\"\",\"gate_receipts\":[\"out/state/gate_receipts/20261030T052821.064096Z.json\"]}\np['P5']={\"status\":\"passed\",\"checks\":[\"report.md with 6 sections citing results keys\",\"claims.json 7 claims, keys resolve\",\"final gate run (receipt appended in gate_receipts list)\"],\"notes\":\"\",\"gate_receipts\":[]}\njson.dump(p,open('out/state/phases.json','w'),indent=1)\nEOF\necho \"- 2026-10-30 P4 passed (gate receipt 20261030T052821, only failure non-terminal phases). P5 report/claims written; final gate run.\" >> out/state/log.md\n~/wp29/venv/bin/python ~/wp29/gates/pgar_gate.py . | grep -E \"GATE|FAIL|receipt\"","description":"Finalize phases and rerun gate"},"caller":{"type":"direct"}}],"container":null,"stop_reason":"tool_use","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":5287,"cache_read_input_tokens":40361,"output_tokens":575,"output_tokens_details":{"thinking_tokens":44},"server_tool_use":{"web_search_requests":0,"web_fetch_requests":0},"service_tier":"standard","cache_creation":{"ephemeral_1h_input_tokens":0,"ephemeral_5m_input_tokens":5287},"inference_geo":"global","iterations":[],"speed":"standard","fallback_credit":null},"input_transformations":[],"diagnostics":null,"context_management":null},"apiBlockIndex":2,"requestId":"req_011CgXsNwLd1LvDi1RrkjkiR","requestRef":"ed62dba7-de53-4589-aa4d-bf60cccebf11","requestedModel":"claude-opus-5-5","type":"assistant","uuid":"913960fd-0338-4973-97a8-befff81429e1","timestamp":"2026-10-30T05:28:26.570Z","effort":"medium","perTurnEffort":"medium","userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0288","sessionId":"6a59845e-54b1-4582-86f5-752e1f4df7e4","version":"2.1.314","gitBranch":"HEAD"}72{"parentUuid":"913960fd-0338-4973-97a8-befff81429e1","isSidechain":false,"promptId":"214a8ece-2bbc-459c-aa1a-66062e2a4650","type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_01CKhwdi2JBaApk9FGjvDxAL","type":"tool_result","content":"PGAR GATE pgar_gate/1.0 — PASSED (0 of 48 checks failed)\nreceipt: /Users/simon-pierreboucher/wp29/runs/RC0288/out/state/gate_receipts/20261030T052826.684003Z.json","is_error":false}]},"uuid":"0626ab9f-7a64-43c5-b734-46e7e9f5fa23","timestamp":"2026-10-30T05:28:26.688Z","toolUseResult":{"stdout":"PGAR GATE pgar_gate/1.0 — PASSED (0 of 48 checks failed)\nreceipt: /Users/simon-pierreboucher/wp29/runs/RC0288/out/state/gate_receipts/20261030T052826.684003Z.json","stderr":"","interrupted":false,"isImage":false,"noOutputExpected":false},"sandboxed":false,"sourceToolAssistantUUID":"913960fd-0338-4973-97a8-befff81429e1","permissionDecision":{"decision":"accept","source":"config","reasonType":"rule","ruleSource":"cliArg"},"userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0288","sessionId":"6a59845e-54b1-4582-86f5-752e1f4df7e4","version":"2.1.314","gitBranch":"HEAD"}73{"type":"api-request-blob","sessionId":"6a59845e-54b1-4582-86f5-752e1f4df7e4","hash":"2e9994c3f9016519324bc3d712ca2653026c3ae7898aba1ad24d210627508eeb","message":{"role":"assistant","content":[{"type":"thinking","thinking":"","signature":"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"},{"type":"text","text":"The only gate failure is that P4 and P5 are not finished yet, which is expected at this point. 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Re-running `out/analysis.py` from scratch produces an identical `out/results.json`.\n\n**Drug consumption room study (Armendariz), log points**\n\n| Quantity | Table | Estimate | Standard error |\n|---|---|---|---|\n| Overall effect, 5 km | 6 | −0.431 (significant at 5%) | 0.210 |\n| Overall effect, 3 km | 6 | −0.283 | 0.279 |\n| Narcotics, 3 km | 7 | 0.549 (significant at 1%) | 0.187 |\n| Fatal drug-related, 1 km | 7 | −1.207 (significant at 1%) | 0.137 |\n| HIV, 5 km | 7 | −0.340 | 0.294 |\n\n- **Observations at 5 km:** 17,250 region-years.\n- **HIV 5 km p-value:** set to null. The paper prints only significance stars, not exact p-values. This estimate has no star, so it is not significant at 10%.\n- **Derived 95% interval for the 5 km effect:** [−0.8426, −0.0194], computed as estimate ± 1.96 × standard error.\n\n**Rhode Island reemployment programme study (Li et al.), Table 2**\n- **Wage effect:** $1,153 per year, with printed 95% interval [166, 2,141].\n- **Wage standard error:** not printed. The interval implies about 503.83, using (upper − lower) / (2 × 1.96). As a check, that value gives p ≈ 0.022, which matches the printed p-value.\n- **Control-group mean wage:** $33,043.\n- **Reemployment effect:** 1.47 percentage points.\n- **Weeks on unemployment insurance:** −1.99 weeks, with interval [−2.24, −1.75].\n\n**Problems found in the sources** (recorded in `data_issues`; none changes an extracted value):\n- **Conflicting standard errors:** the same 5 km effect has a standard error of 0.210 in Table 6 but 0.218 in Appendix Table A19. I used Table 6, as the task specifies.\n- **Control-region count:** 608 in the main tables, 640 in the appendix table note.\n- **Fatal-hospitalisation figure:** the abstract says a 35% fall; the body text says about 30%.\n- **Weeks-on-insurance interval:** the Rhode Island text gives it without signs, while Table 2 gives the signed values I used.\n- **Rendering defect:** the text shows \"p ¡ 0.001\" where it means p < 0.001.\n\n**Limitations:**\n- Both derived values assume an approximately normal estimator with critical value 1.96; the wage standard error also assumes the printed interval is symmetric.\n- JSON drops trailing zeros, so 0.210 is stored as 0.21 and −0.340 as −0.34. The values are numerically identical, and the script prints the original strings.\n\nEverything is in `out/`; open `report.md` first. 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