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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"}],"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/RC0284","sessionId":"dada2278-6d32-4907-8ce0-67560ca80944","version":"2.1.314","gitBranch":"HEAD"}11{"type":"tools-used","tools":["Bash"],"sessionId":"dada2278-6d32-4907-8ce0-67560ca80944"}12{"parentUuid":"35f2e49d-6bb6-4e17-9155-d2a3880db184","isSidechain":false,"message":{"model":"claude-opus-5-5","id":"msg_011CgXs2PUADrB6ESB3usHCf","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":2209,"cache_read_input_tokens":1504,"output_tokens":129,"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":2209},"inference_geo":"global","iterations":[],"speed":"standard","fallback_credit":null},"input_transformations":[],"diagnostics":null,"context_management":null},"thinkingDurationMs":460,"thinkingDisplay":"updates","apiBlockIndex":0,"requestId":"req_011CgXs2PAKVz5i3fVUp4zVo","requestRef":"2d82cc97-7e20-40e7-9816-12f0fb37e881","requestedModel":"claude-opus-5-5","type":"assistant","uuid":"2b0997f7-3831-4d4a-b032-914560623258","timestamp":"2026-10-30T05:23:43.553Z","effort":"medium","perTurnEffort":"medium","userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0284","sessionId":"dada2278-6d32-4907-8ce0-67560ca80944","version":"2.1.314","gitBranch":"HEAD"}13{"parentUuid":"2b0997f7-3831-4d4a-b032-914560623258","isSidechain":false,"message":{"model":"claude-opus-5-5","id":"msg_011CgXs2PUADrB6ESB3usHCf","type":"message","role":"assistant","content":[{"type":"tool_use","id":"toolu_01RUMumuYpKrj7x4RHUExPDD","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 158 simon-pierreboucher staff 5056 oct. 30 01:23 ..\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\ndata:\nREADME.md\nsources\n\ndata/sources:\narxiv-2606.14621v1.txt\narxiv-2609.27732v1.txt","is_error":false}]},"uuid":"51c0eb66-b426-4085-996f-f272f185a38c","timestamp":"2026-10-30T05:23:44.192Z","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 158 simon-pierreboucher staff 5056 oct. 30 01:23 ..\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\ndata:\nREADME.md\nsources\n\ndata/sources:\narxiv-2606.14621v1.txt\narxiv-2609.27732v1.txt","stderr":"","interrupted":false,"isImage":false,"noOutputExpected":false},"sandboxed":false,"sourceToolAssistantUUID":"fd71cd3e-223e-4c2f-be33-29a9a394901c","permissionDecision":{"decision":"accept","source":"config","reasonType":"rule","ruleSource":"cliArg"},"userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0284","sessionId":"dada2278-6d32-4907-8ce0-67560ca80944","version":"2.1.314","gitBranch":"HEAD"}15{"type":"api-request-blob","sessionId":"dada2278-6d32-4907-8ce0-67560ca80944","hash":"39de1b77a9e1b2bc66ee31c02611458460b374fd7164ee80a835ad03ca857d8d","message":{"role":"assistant","content":[{"type":"thinking","thinking":"","signature":"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"},{"type":"tool_use","id":"toolu_01RUMumuYpKrj7x4RHUExPDD","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 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\nhead: illegal line count -- 0\nSOURCE EXTRACT — arxiv-2609.27732v1\nTitle: Do Drug Consumption Rooms Reduce Drug-Related Hospitalizations? Evidence from Switzerland\nAuthors: Ana Armendariz (University of St. Gallen)\nBibliographic 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\nRetrieved: https://arxiv.org/html/2609.27732v1 on 2026-10-30T03:23:18Z (SHA-256 of the retrieved HTML: 22b06050f4ff6e665aedd80561fa4031b1717867ecfeca71d1d2902ee1b09f52)\nLicense: CC BY 4.0 (license shown on the arXiv abstract page; https://creativecommons.org/licenses/by/4.0/). Reproduced in part with attribution.\nExtract 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.\nOmitted: Sections 1-5 and 7, the other appendix tables and figures, the references.\nRendering 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================================================================\n\n# Do Drug Consumption Rooms Reduce Drug-Related Hospitalizations? Evidence from Switzerland\n\nAna Armendariz*\n\nSeptember 23, 2026\n\nThis 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\nKeywords: Drug Consumption Rooms, Harm Reduction, Hospitalizations, Drugs, Public Health, Causal Inference\n\n## 6 Results\n\nThis 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\nFigure 6: Event study estimates by buffer zone, \\pm 5-year window\n\nNote: 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\nFigure 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\nTable 6: Overall Average Treatment Effect on the Treated (ATT)\n\n| 1 km | 3 km | 5 km\n\nATT | -0.399 | -0.283 | -0.431**\n\n| (0.412) | (0.279) | (0.210)\n\n% change | -32.9% | -24.7% | -35.0%\n\nTreated regions | 7 | 35 | 50\n\nControl regions | 608 | 608 | 608\n\nObservations | 16,175 | 16,875 | 17,250\n\nNote: 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\nthroughout. Regions near DCRs that opened before 1998\n(always-treated) are excluded. Standard errors clustered at the Medstat region level in\nparentheses. Significance: {}^{***}p<0.01 , {}^{**}p<0.05 ,\n{}^{*}p<0.10 .\n\nTable 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\ncorresponding 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\nTo further explore the mechanisms through which DCR openings affect drug-related hospitalizations, I disaggregate the main outcome into five diagnostic\nsub-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\nFigure 7: Event study: Sub-outcome estimates,\nwithin 5 km, \\pm 5-year window\n\nNote: 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\nDCR 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\nTable 7: Overall ATT by Outcome Category and Buffer Zone\n\nOutcome | 1 km | 3 km | 5 km\n\nMental/Behavioral | -0.354 | -0.202 | -0.381*\n\n| (0.382) | (0.244) | (0.220)\n\nHepatitis | -0.270 | -0.413 | -0.406\n\n| (0.486) | (0.270) | (0.250)\n\nHIV | 0.063 | -0.239 | -0.340\n\n| (0.801) | (0.337) | (0.294)\n\nNarcotics | 1.186*** | 0.549*** | 0.390**\n\n| (0.288) | (0.187) | (0.165)\n\nDrug-related (fatal) | -1.207*** | -0.290 | -0.354**\n\n| (0.137) | (0.215) | (0.178)\n\nTreated regions | 7 | 35 | 50\n\nControl regions | 608 (never-treated) | |\n\nNote: Each cell reports the overall ATT from\nthe doubly robust Callaway & Sant’Anna (2021) estimator with\nnever-treated controls and a universal base period. Outcomes are\nlog hospitalization rates per 100,000 inhabitants. Sub-outcomes:\nmental and behavioral disorders (F11–F19), viral hepatitis (B15–B19),\nHIV disease (B20–B24), narcotics/psychodysleptics (T40). The 1 km\nestimates are identified from a single treated region per cohort and\nshould be interpreted with caution. Regions near DCRs opened before\n1998 excluded. Standard errors clustered at the Medstat region level.\nSignificance: {}^{***}p<0.01 , {}^{**}p<0.05 , {}^{*}p<0.10 .\n\nFigure 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\n### 6.1 Mechanisms\n\nThe 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\nsubstitution 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(Kerr et al., 2017; Hedrich, 2004).\n\nThe 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\nA 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\nTable A19: Robustness: Overall ATT Across Estimators — 5 km buffer, \\pm 5-year window\n\n| CS-DR | Sun & Abraham | Synthetic DiD | Stacked DiD\n\nATT | -0.431** | -0.438** | -0.362* | -0.149***\n\n| (0.218) | (0.208) | (0.205) | (0.038)\n\n% change | -35.0% | -35.5% | -30.3% | -13.9%\n\nTreated regions | 50 | | |\n\nControl regions | 608 | | |\n\nObservations | 17,250 | | |\n\nNote: All specifications use the 5 km buffer,\nnever-treated comparison regions (640 regions), and a \\pm 5-year window","is_error":false}]},"uuid":"cf807176-7a6c-480e-84e1-62e68607cc6e","timestamp":"2026-10-30T05:23:46.827Z","toolUseResult":{"stdout":"# 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 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\nhead: illegal line count -- 0\nSOURCE EXTRACT — arxiv-2609.27732v1\nTitle: Do Drug Consumption Rooms Reduce Drug-Related Hospitalizations? Evidence from Switzerland\nAuthors: Ana Armendariz (University of St. Gallen)\nBibliographic 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\nRetrieved: https://arxiv.org/html/2609.27732v1 on 2026-10-30T03:23:18Z (SHA-256 of the retrieved HTML: 22b06050f4ff6e665aedd80561fa4031b1717867ecfeca71d1d2902ee1b09f52)\nLicense: CC BY 4.0 (license shown on the arXiv abstract page; https://creativecommons.org/licenses/by/4.0/). Reproduced in part with attribution.\nExtract 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.\nOmitted: Sections 1-5 and 7, the other appendix tables and figures, the references.\nRendering 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================================================================\n\n# Do Drug Consumption Rooms Reduce Drug-Related Hospitalizations? Evidence from Switzerland\n\nAna Armendariz*\n\nSeptember 23, 2026\n\nThis 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\nKeywords: Drug Consumption Rooms, Harm Reduction, Hospitalizations, Drugs, Public Health, Causal Inference\n\n## 6 Results\n\nThis 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\nFigure 6: Event study estimates by buffer zone, \\pm 5-year window\n\nNote: 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\nFigure 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\nTable 6: Overall Average Treatment Effect on the Treated (ATT)\n\n| 1 km | 3 km | 5 km\n\nATT | -0.399 | -0.283 | -0.431**\n\n| (0.412) | (0.279) | (0.210)\n\n% change | -32.9% | -24.7% | -35.0%\n\nTreated regions | 7 | 35 | 50\n\nControl regions | 608 | 608 | 608\n\nObservations | 16,175 | 16,875 | 17,250\n\nNote: 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\nthroughout. Regions near DCRs that opened before 1998\n(always-treated) are excluded. Standard errors clustered at the Medstat region level in\nparentheses. Significance: {}^{***}p<0.01 , {}^{**}p<0.05 ,\n{}^{*}p<0.10 .\n\nTable 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\ncorresponding 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\nTo further explore the mechanisms through which DCR openings affect drug-related hospitalizations, I disaggregate the main outcome into five diagnostic\nsub-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\nFigure 7: Event study: Sub-outcome estimates,\nwithin 5 km, \\pm 5-year window\n\nNote: 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\nDCR 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\nTable 7: Overall ATT by Outcome Category and Buffer Zone\n\nOutcome | 1 km | 3 km | 5 km\n\nMental/Behavioral | -0.354 | -0.202 | -0.381*\n\n| (0.382) | (0.244) | (0.220)\n\nHepatitis | -0.270 | -0.413 | -0.406\n\n| (0.486) | (0.270) | (0.250)\n\nHIV | 0.063 | -0.239 | -0.340\n\n| (0.801) | (0.337) | (0.294)\n\nNarcotics | 1.186*** | 0.549*** | 0.390**\n\n| (0.288) | (0.187) | (0.165)\n\nDrug-related (fatal) | -1.207*** | -0.290 | -0.354**\n\n| (0.137) | (0.215) | (0.178)\n\nTreated regions | 7 | 35 | 50\n\nControl regions | 608 (never-treated) | |\n\nNote: Each cell reports the overall ATT from\nthe doubly robust Callaway & Sant’Anna (2021) estimator with\nnever-treated controls and a universal base period. Outcomes are\nlog hospitalization rates per 100,000 inhabitants. Sub-outcomes:\nmental and behavioral disorders (F11–F19), viral hepatitis (B15–B19),\nHIV disease (B20–B24), narcotics/psychodysleptics (T40). The 1 km\nestimates are identified from a single treated region per cohort and\nshould be interpreted with caution. Regions near DCRs opened before\n1998 excluded. Standard errors clustered at the Medstat region level.\nSignificance: {}^{***}p<0.01 , {}^{**}p<0.05 , {}^{*}p<0.10 .\n\nFigure 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\n### 6.1 Mechanisms\n\nThe 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\nsubstitution 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(Kerr et al., 2017; Hedrich, 2004).\n\nThe 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\nA 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\nTable A19: Robustness: Overall ATT Across Estimators — 5 km buffer, \\pm 5-year window\n\n| CS-DR | Sun & Abraham | Synthetic DiD | Stacked DiD\n\nATT | -0.431** | -0.438** | -0.362* | -0.149***\n\n| (0.218) | (0.208) | (0.205) | (0.038)\n\n% change | -35.0% | -35.5% | -30.3% | -13.9%\n\nTreated regions | 50 | | |\n\nControl regions | 608 | | |\n\nObservations | 17,250 | | |\n\nNote: All specifications use the 5 km buffer,\nnever-treated comparison regions (640 regions), and a \\pm 5-year window","stderr":"","interrupted":false,"isImage":false,"noOutputExpected":false},"sandboxed":false,"sourceToolAssistantUUID":"32ce4d00-20f0-48de-b463-503305cb1dca","permissionDecision":{"decision":"accept","source":"config","reasonType":"rule","ruleSource":"cliArg"},"userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0284","sessionId":"dada2278-6d32-4907-8ce0-67560ca80944","version":"2.1.314","gitBranch":"HEAD"}21{"parentUuid":"cf807176-7a6c-480e-84e1-62e68607cc6e","isSidechain":false,"message":{"model":"claude-opus-5-5","id":"msg_011CgXs2XqT7dmm8zNpDLeZ7","type":"message","role":"assistant","content":[{"type":"tool_use","id":"toolu_01MQg34uGwDiLF93S6um5SR6","name":"Bash","input":{"command":"cat data/sources/arxiv-2606.14621v1.txt","description":"Read RESEA source"},"caller":{"type":"direct"}}],"container":null,"stop_reason":"tool_use","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":3673,"cache_read_input_tokens":3713,"output_tokens":269,"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":3673},"inference_geo":"global","iterations":[],"speed":"standard","fallback_credit":null},"input_transformations":[],"diagnostics":null,"context_management":null},"apiBlockIndex":1,"requestId":"req_011CgXs2XY6tsUSHVaaA787g","requestRef":"3349bba2-5f04-456e-a14f-30e68d791f01","requestedModel":"claude-opus-5-5","type":"assistant","uuid":"f8fb7e56-ee50-4126-8374-8fe961060517","timestamp":"2026-10-30T05:23:47.248Z","effort":"medium","perTurnEffort":"medium","userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0284","sessionId":"dada2278-6d32-4907-8ce0-67560ca80944","version":"2.1.314","gitBranch":"HEAD"}22{"parentUuid":"f8fb7e56-ee50-4126-8374-8fe961060517","isSidechain":false,"promptId":"52960967-79c0-4a18-8c3d-73f4cb1f259b","type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_01MQg34uGwDiLF93S6um5SR6","type":"tool_result","content":"SOURCE EXTRACT — arxiv-2606.14621v1\nTitle: Evaluating the Impact of Rhode Island's Self-Sustaining Reemployment Services and Eligibility Assessment (RESEA) Program on Employment Outcomes\nAuthors: Harrison H. Li; Shanna Pearson-Merkowitz; David Yokum\nBibliographic 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\nRetrieved: https://arxiv.org/html/2606.14621v1 on 2026-10-30T03:23:18Z (SHA-256 of the retrieved HTML: f98fb1b39edd2107051cddf1da34aa8cf19e74e04d60bf9305a88cf966417da1)\nLicense: CC BY 4.0 (license shown on the arXiv abstract page; https://creativecommons.org/licenses/by/4.0/). Reproduced in part with attribution.\nExtract 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.\nOmitted: Sections 1-2, Table 1, Section 3.2 (heterogeneous effects), the rest of Section 4, the references and the appendix.\nRendering 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.\nEditorial 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================================================================\n\n# Evaluating the Impact of Rhode Island's Self-Sustaining Reemployment Services and Eligibility Assessment (RESEA) Program on Employment Outcomes\n\nHarrison H. Li (Harvey Mudd College), Shanna Pearson-Merkowitz (University of Maryland, College Park), David Yokum (University of North Carolina at Chapel Hill)\n\n###### Abstract\n\nProlonged 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\n## 3 Results\n\n[Table 1 (pre-treatment characteristics) omitted from this extract.]\n\n[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\nOutcome | Treatment | Control | Treatment-control difference | Average treatment effect estimate\nWage | $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]\nReemployment | 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]\nWeeks 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]\nShort-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]\nShort-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]\nShort-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\nTable 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\nOverall, 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\nThe 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\nAs 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\n### 3.1 Cost-benefit analysis\n\nWe 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\n### 4.1 Methodological lessons (excerpt)\n\n[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\nOutcome | Difference in means | Covariate adjusted | Fixed effects regression\nWage | $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]\nReemployment | 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]\nWeeks 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\nTable 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\nIf 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","is_error":false}]},"uuid":"0c203c8c-de61-40e4-bb9e-8f644d9e4cc6","timestamp":"2026-10-30T05:23:47.286Z","toolUseResult":{"stdout":"SOURCE EXTRACT — arxiv-2606.14621v1\nTitle: Evaluating the Impact of Rhode Island's Self-Sustaining Reemployment Services and Eligibility Assessment (RESEA) Program on Employment Outcomes\nAuthors: Harrison H. Li; Shanna Pearson-Merkowitz; David Yokum\nBibliographic 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\nRetrieved: https://arxiv.org/html/2606.14621v1 on 2026-10-30T03:23:18Z (SHA-256 of the retrieved HTML: f98fb1b39edd2107051cddf1da34aa8cf19e74e04d60bf9305a88cf966417da1)\nLicense: CC BY 4.0 (license shown on the arXiv abstract page; https://creativecommons.org/licenses/by/4.0/). Reproduced in part with attribution.\nExtract 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.\nOmitted: Sections 1-2, Table 1, Section 3.2 (heterogeneous effects), the rest of Section 4, the references and the appendix.\nRendering 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.\nEditorial 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================================================================\n\n# Evaluating the Impact of Rhode Island's Self-Sustaining Reemployment Services and Eligibility Assessment (RESEA) Program on Employment Outcomes\n\nHarrison H. Li (Harvey Mudd College), Shanna Pearson-Merkowitz (University of Maryland, College Park), David Yokum (University of North Carolina at Chapel Hill)\n\n###### Abstract\n\nProlonged 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\n## 3 Results\n\n[Table 1 (pre-treatment characteristics) omitted from this extract.]\n\n[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\nOutcome | Treatment | Control | Treatment-control difference | Average treatment effect estimate\nWage | $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]\nReemployment | 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]\nWeeks 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]\nShort-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]\nShort-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]\nShort-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\nTable 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\nOverall, 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\nThe 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\nAs 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\n### 3.1 Cost-benefit analysis\n\nWe 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\n### 4.1 Methodological lessons (excerpt)\n\n[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\nOutcome | Difference in means | Covariate adjusted | Fixed effects regression\nWage | $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]\nReemployment | 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]\nWeeks 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\nTable 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\nIf 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","stderr":"","interrupted":false,"isImage":false,"noOutputExpected":false},"sandboxed":false,"sourceToolAssistantUUID":"f8fb7e56-ee50-4126-8374-8fe961060517","permissionDecision":{"decision":"accept","source":"config","reasonType":"rule","ruleSource":"cliArg"},"userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0284","sessionId":"dada2278-6d32-4907-8ce0-67560ca80944","version":"2.1.314","gitBranch":"HEAD"}23{"type":"api-request-blob","sessionId":"dada2278-6d32-4907-8ce0-67560ca80944","hash":"9f5058385c2b66d1b67df67f2bb33691651cc971442f49e447b59afb37f4a231","message":{"role":"assistant","content":[{"type":"tool_use","id":"toolu_01PjnQKoCDxodg9x5EMN3GQe","name":"Bash","input":{"command":"cat data/README.md; wc -l data/README.md data/sources/*; shasum -a 256 data/README.md data/sources/*; cat -A data/sources/arxiv-2609.27732v1.txt 2>/dev/null | head -0; cat data/sources/arxiv-2609.27732v1.txt","description":"Read README and DCR source"}},{"type":"tool_use","id":"toolu_01MQg34uGwDiLF93S6um5SR6","name":"Bash","input":{"command":"cat data/sources/arxiv-2606.14621v1.txt","description":"Read RESEA source"}}]}}24{"type":"api-request-blob","sessionId":"dada2278-6d32-4907-8ce0-67560ca80944","hash":"5942fd42705f240bcb1f961097f72a76f65aeb8662ec03578987f47c53f6a6f9","message":{"role":"user","content":[{"tool_use_id":"toolu_01PjnQKoCDxodg9x5EMN3GQe","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 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\nhead: illegal line count -- 0\nSOURCE EXTRACT — arxiv-2609.27732v1\nTitle: Do Drug Consumption Rooms Reduce Drug-Related Hospitalizations? Evidence from Switzerland\nAuthors: Ana Armendariz (University of St. Gallen)\nBibliographic 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\nRetrieved: https://arxiv.org/html/2609.27732v1 on 2026-10-30T03:23:18Z (SHA-256 of the retrieved HTML: 22b06050f4ff6e665aedd80561fa4031b1717867ecfeca71d1d2902ee1b09f52)\nLicense: CC BY 4.0 (license shown on the arXiv abstract page; https://creativecommons.org/licenses/by/4.0/). Reproduced in part with attribution.\nExtract 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.\nOmitted: Sections 1-5 and 7, the other appendix tables and figures, the references.\nRendering 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================================================================\n\n# Do Drug Consumption Rooms Reduce Drug-Related Hospitalizations? Evidence from Switzerland\n\nAna Armendariz*\n\nSeptember 23, 2026\n\nThis 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\nKeywords: Drug Consumption Rooms, Harm Reduction, Hospitalizations, Drugs, Public Health, Causal Inference\n\n## 6 Results\n\nThis 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\nFigure 6: Event study estimates by buffer zone, \\pm 5-year window\n\nNote: 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\nFigure 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\nTable 6: Overall Average Treatment Effect on the Treated (ATT)\n\n| 1 km | 3 km | 5 km\n\nATT | -0.399 | -0.283 | -0.431**\n\n| (0.412) | (0.279) | (0.210)\n\n% change | -32.9% | -24.7% | -35.0%\n\nTreated regions | 7 | 35 | 50\n\nControl regions | 608 | 608 | 608\n\nObservations | 16,175 | 16,875 | 17,250\n\nNote: 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\nthroughout. Regions near DCRs that opened before 1998\n(always-treated) are excluded. Standard errors clustered at the Medstat region level in\nparentheses. Significance: {}^{***}p<0.01 , {}^{**}p<0.05 ,\n{}^{*}p<0.10 .\n\nTable 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\ncorresponding 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\nTo further explore the mechanisms through which DCR openings affect drug-related hospitalizations, I disaggregate the main outcome into five diagnostic\nsub-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\nFigure 7: Event study: Sub-outcome estimates,\nwithin 5 km, \\pm 5-year window\n\nNote: 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\nDCR 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\nTable 7: Overall ATT by Outcome Category and Buffer Zone\n\nOutcome | 1 km | 3 km | 5 km\n\nMental/Behavioral | -0.354 | -0.202 | -0.381*\n\n| (0.382) | (0.244) | (0.220)\n\nHepatitis | -0.270 | -0.413 | -0.406\n\n| (0.486) | (0.270) | (0.250)\n\nHIV | 0.063 | -0.239 | -0.340\n\n| (0.801) | (0.337) | (0.294)\n\nNarcotics | 1.186*** | 0.549*** | 0.390**\n\n| (0.288) | (0.187) | (0.165)\n\nDrug-related (fatal) | -1.207*** | -0.290 | -0.354**\n\n| (0.137) | (0.215) | (0.178)\n\nTreated regions | 7 | 35 | 50\n\nControl regions | 608 (never-treated) | |\n\nNote: Each cell reports the overall ATT from\nthe doubly robust Callaway & Sant’Anna (2021) estimator with\nnever-treated controls and a universal base period. Outcomes are\nlog hospitalization rates per 100,000 inhabitants. Sub-outcomes:\nmental and behavioral disorders (F11–F19), viral hepatitis (B15–B19),\nHIV disease (B20–B24), narcotics/psychodysleptics (T40). The 1 km\nestimates are identified from a single treated region per cohort and\nshould be interpreted with caution. Regions near DCRs opened before\n1998 excluded. Standard errors clustered at the Medstat region level.\nSignificance: {}^{***}p<0.01 , {}^{**}p<0.05 , {}^{*}p<0.10 .\n\nFigure 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\n### 6.1 Mechanisms\n\nThe 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\nsubstitution 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(Kerr et al., 2017; Hedrich, 2004).\n\nThe 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\nA 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\nTable A19: Robustness: Overall ATT Across Estimators — 5 km buffer, \\pm 5-year window\n\n| CS-DR | Sun & Abraham | Synthetic DiD | Stacked DiD\n\nATT | -0.431** | -0.438** | -0.362* | -0.149***\n\n| (0.218) | (0.208) | (0.205) | (0.038)\n\n% change | -35.0% | -35.5% | -30.3% | -13.9%\n\nTreated regions | 50 | | |\n\nControl regions | 608 | | |\n\nObservations | 17,250 | | |\n\nNote: All specifications use the 5 km buffer,\nnever-treated comparison regions (640 regions), and a \\pm 5-year window","is_error":false},{"tool_use_id":"toolu_01MQg34uGwDiLF93S6um5SR6","type":"tool_result","content":"SOURCE EXTRACT — arxiv-2606.14621v1\nTitle: Evaluating the Impact of Rhode Island's Self-Sustaining Reemployment Services and Eligibility Assessment (RESEA) Program on Employment Outcomes\nAuthors: Harrison H. Li; Shanna Pearson-Merkowitz; David Yokum\nBibliographic 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\nRetrieved: https://arxiv.org/html/2606.14621v1 on 2026-10-30T03:23:18Z (SHA-256 of the retrieved HTML: f98fb1b39edd2107051cddf1da34aa8cf19e74e04d60bf9305a88cf966417da1)\nLicense: CC BY 4.0 (license shown on the arXiv abstract page; https://creativecommons.org/licenses/by/4.0/). Reproduced in part with attribution.\nExtract 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.\nOmitted: Sections 1-2, Table 1, Section 3.2 (heterogeneous effects), the rest of Section 4, the references and the appendix.\nRendering 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.\nEditorial 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================================================================\n\n# Evaluating the Impact of Rhode Island's Self-Sustaining Reemployment Services and Eligibility Assessment (RESEA) Program on Employment Outcomes\n\nHarrison H. Li (Harvey Mudd College), Shanna Pearson-Merkowitz (University of Maryland, College Park), David Yokum (University of North Carolina at Chapel Hill)\n\n###### Abstract\n\nProlonged 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\n## 3 Results\n\n[Table 1 (pre-treatment characteristics) omitted from this extract.]\n\n[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\nOutcome | Treatment | Control | Treatment-control difference | Average treatment effect estimate\nWage | $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]\nReemployment | 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]\nWeeks 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]\nShort-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]\nShort-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]\nShort-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\nTable 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\nOverall, 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\nThe 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\nAs 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\n### 3.1 Cost-benefit analysis\n\nWe 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\n### 4.1 Methodological lessons (excerpt)\n\n[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\nOutcome | Difference in means | Covariate adjusted | Fixed effects regression\nWage | $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]\nReemployment | 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]\nWeeks 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\nTable 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\nIf 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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P0 contract and ledger."}],"container":null,"stop_reason":"tool_use","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":2795,"cache_read_input_tokens":18361,"output_tokens":965,"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":2795},"inference_geo":"global","iterations":[],"speed":"standard","fallback_credit":null},"input_transformations":[],"diagnostics":null,"context_management":null},"apiBlockIndex":0,"requestId":"req_011CgXs3zv9s13jMjNo62y1s","requestRef":"7bccea36-64d8-4ae6-ab7f-012c8942bc95","requestedModel":"claude-opus-5-5","type":"assistant","uuid":"00d86f96-e4b4-4f31-89f1-f5bce22c27be","timestamp":"2026-10-30T05:24:07.025Z","effort":"medium","perTurnEffort":"medium","userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0284","sessionId":"dada2278-6d32-4907-8ce0-67560ca80944","version":"2.1.314","gitBranch":"HEAD"}34{"parentUuid":"00d86f96-e4b4-4f31-89f1-f5bce22c27be","isSidechain":false,"message":{"model":"claude-opus-5-5","id":"msg_011CgXs41aLSy1UJ1VboSK1o","type":"message","role":"assistant","content":[{"type":"tool_use","id":"toolu_011rPQMsX3P93Fi6oJRVVMD9","name":"Bash","input":{"command":"cat > out/state/contract.md <<'EOF'\n# Contract — F4-conf-01\n\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 from a printed SE (DCR 5 km ATT, Table 6) and (b) an SE from a printed 95% CI (RESEA wage ATE, Table 2), and identify which requested quantity is not reported (exact p-value of DCR HIV 5 km ATT).\n\n**Estimands.** The printed numbers themselves (no re-estimation). Derived quantities are meaningful only under an approximate-normal sampling distribution of the estimator (symmetric Wald interval, critical value 1.96).\n\n**Outputs.** out/results.json (25 content keys listed in TASK.md + locators, derivation_notes, task_id, feasibility, feasibility_reason, data_issues, n_input_rows, n_analysed, random_seed), out/report.md (Question; Data and exclusions; Method; Results; Uncertainty; Limitations and open issues), out/analysis.py (regenerates results.json from data/ only, prints matched lines), out/claims.json.\n\n**Information rules.** Only the two extracts are admissible; no memory of the papers. Values as printed (sign, decimals; thousands separators removed). Where a value appears in several tables, use the table named in the key (e.g. Table 6 SE 0.210, not Table A19 0.218; Table 2 not Table 3). Derived keys state convention. Unreported quantities → null with \"not reported in <source>\" locator.\n\n**Invalidity / impossibility.** Missing tables/cells in extracts; ambiguous cell attribution; using non-named table; inventing an exact p-value from stars.\nEOF\ncat > out/state/phases.json <<'EOF'\n{\"P0\":{\"status\":\"passed\",\"checks\":[\"TASK.md read fully\",\"contract.md written\"],\"notes\":\"\"},\n \"P1\":{\"status\":\"pending\",\"checks\":[],\"notes\":\"\"},\n \"P2\":{\"status\":\"pending\",\"checks\":[],\"notes\":\"\"},\n \"P3\":{\"status\":\"pending\",\"checks\":[],\"notes\":\"\"},\n \"P4\":{\"status\":\"pending\",\"checks\":[],\"notes\":\"\"},\n \"P5\":{\"status\":\"pending\",\"checks\":[],\"notes\":\"\"}}\nEOF\necho \"- 2026-10-30 P0 passed: contract written.\" > out/state/log.md","description":"Write contract, phase ledger and log"},"caller":{"type":"direct"}}],"container":null,"stop_reason":"tool_use","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":2795,"cache_read_input_tokens":18361,"output_tokens":965,"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":2795},"inference_geo":"global","iterations":[],"speed":"standard","fallback_credit":null},"input_transformations":[],"diagnostics":null,"context_management":null},"apiBlockIndex":1,"requestId":"req_011CgXs3zv9s13jMjNo62y1s","requestRef":"7bccea36-64d8-4ae6-ab7f-012c8942bc95","requestedModel":"claude-opus-5-5","type":"assistant","uuid":"926350eb-e91b-4096-a7b8-06382302a5e2","timestamp":"2026-10-30T05:24:14.440Z","effort":"medium","perTurnEffort":"medium","userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0284","sessionId":"dada2278-6d32-4907-8ce0-67560ca80944","version":"2.1.314","gitBranch":"HEAD"}35{"parentUuid":"926350eb-e91b-4096-a7b8-06382302a5e2","isSidechain":false,"promptId":"52960967-79c0-4a18-8c3d-73f4cb1f259b","type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_011rPQMsX3P93Fi6oJRVVMD9","type":"tool_result","content":"(Bash completed with no output)","is_error":false}]},"uuid":"0260459c-3ebb-47f3-9f09-f0c1fdb985dc","timestamp":"2026-10-30T05:24:14.474Z","toolUseResult":{"stdout":"","stderr":"","interrupted":false,"isImage":false,"noOutputExpected":false},"sandboxed":false,"sourceToolAssistantUUID":"926350eb-e91b-4096-a7b8-06382302a5e2","permissionDecision":{"decision":"accept","source":"config","reasonType":"rule","ruleSource":"cliArg"},"userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0284","sessionId":"dada2278-6d32-4907-8ce0-67560ca80944","version":"2.1.314","gitBranch":"HEAD"}36{"type":"api-request-blob","sessionId":"dada2278-6d32-4907-8ce0-67560ca80944","hash":"04f4852738eda66a2798171a084e014f54156eff0b27e56a01996164d22eacc4","message":{"role":"assistant","content":[{"type":"text","text":"Now P0 contract and ledger."},{"type":"tool_use","id":"toolu_011rPQMsX3P93Fi6oJRVVMD9","name":"Bash","input":{"command":"cat > out/state/contract.md <<'EOF'\n# Contract — F4-conf-01\n\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 from a printed SE (DCR 5 km ATT, Table 6) and (b) an SE from a printed 95% CI (RESEA wage ATE, Table 2), and identify which requested quantity is not reported (exact p-value of DCR HIV 5 km ATT).\n\n**Estimands.** The printed numbers themselves (no re-estimation). Derived quantities are meaningful only under an approximate-normal sampling distribution of the estimator (symmetric Wald interval, critical value 1.96).\n\n**Outputs.** out/results.json (25 content keys listed in TASK.md + locators, derivation_notes, task_id, feasibility, feasibility_reason, data_issues, n_input_rows, n_analysed, random_seed), out/report.md (Question; Data and exclusions; Method; Results; Uncertainty; Limitations and open issues), out/analysis.py (regenerates results.json from data/ only, prints matched lines), out/claims.json.\n\n**Information rules.** Only the two extracts are admissible; no memory of the papers. Values as printed (sign, decimals; thousands separators removed). Where a value appears in several tables, use the table named in the key (e.g. Table 6 SE 0.210, not Table A19 0.218; Table 2 not Table 3). Derived keys state convention. Unreported quantities → null with \"not reported in <source>\" locator.\n\n**Invalidity / impossibility.** Missing tables/cells in extracts; ambiguous cell attribution; using non-named table; inventing an exact p-value from stars.\nEOF\ncat > out/state/phases.json <<'EOF'\n{\"P0\":{\"status\":\"passed\",\"checks\":[\"TASK.md read fully\",\"contract.md written\"],\"notes\":\"\"},\n \"P1\":{\"status\":\"pending\",\"checks\":[],\"notes\":\"\"},\n \"P2\":{\"status\":\"pending\",\"checks\":[],\"notes\":\"\"},\n \"P3\":{\"status\":\"pending\",\"checks\":[],\"notes\":\"\"},\n \"P4\":{\"status\":\"pending\",\"checks\":[],\"notes\":\"\"},\n \"P5\":{\"status\":\"pending\",\"checks\":[],\"notes\":\"\"}}\nEOF\necho \"- 2026-10-30 P0 passed: contract written.\" > out/state/log.md","description":"Write contract, phase ledger and 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> out/analysis.py <<'EOF'\n\"\"\"F4-conf-01: extract printed treatment effects / uncertainty from two arXiv extracts.\n\nReads only data/; writes out/results.json deterministically. No randomness.\nRun from workspace root: ~/wp29/venv/bin/python out/analysis.py\n\"\"\"\nimport json, math, os, re, hashlib\n\nDATA = \"data\"\nDCR_ID, RI_ID = \"arxiv-2609.27732v1\", \"arxiv-2606.14621v1\"\nDCR_F = os.path.join(DATA, \"sources\", DCR_ID + \".txt\")\nRI_F = os.path.join(DATA, \"sources\", RI_ID + \".txt\")\nZ = 1.96 # normal critical value for two-sided 95%\n\n\ndef lines(path):\n with open(path, encoding=\"utf-8\") as f:\n return f.read().split(\"\\n\")\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, $, %, thousands commas, parentheses.\"\"\"\n s = s.replace(\"$\", \"\").replace(\",\", \"\").replace(\"*\", \"\").strip().strip(\"()\")\n return int(s) if re.fullmatch(r\"-?\\d+\", s) else float(s)\n\n\ndef stars(s):\n m = re.search(r\"(\\*+)\\s*$\", s.strip())\n return len(m.group(1)) if m else 0\n\n\ndef table_block(L, title_prefix, end_prefix=\"Note:\"):\n i = next(k for k, l in enumerate(L) if l.startswith(title_prefix))\n j = next(k for k in range(i + 1, len(L)) if L[k].startswith(end_prefix))\n return i, [(k, L[k]) for k in range(i + 1, j) if L[k].strip()]\n\n\ndef reg_table(L, title_prefix):\n \"\"\"Parse a regression table: header, coefficient rows followed by SE rows.\"\"\"\n i, blk = table_block(L, title_prefix)\n hdr = cells(blk[0][1])\n cols = [c for c in hdr if c and c != \"Outcome\"]\n rows, last = {}, None\n for k, l in blk[1:]:\n c = cells(l)\n if c[0] == \"\" and last is not None and c[1].startswith(\"(\"):\n rows[last][\"se\"] = dict(zip(cols, c[1:]))\n rows[last][\"se_line\"] = (k + 1, l)\n else:\n last = c[0]\n rows[last] = {\"est\": dict(zip(cols, c[1:])), \"line\": (k + 1, l)}\n return cols, rows\n\n\ndef show(tag, ln):\n print(f\"[{tag}] line {ln[0]}: {ln[1]}\")\n\n\ndef main():\n DL, RL = lines(DCR_F), lines(RI_F)\n res, loc, issues = {}, {}, []\n\n def L(src, t, row, col, extra=\"\"):\n return f'{src} | Table {t} | row \"{row}\" | column \"{col}\"' + extra\n\n # ---------------- DCR Table 6 ----------------\n c6, t6 = reg_table(DL, \"Table 6:\")\n show(\"T6 ATT\", t6[\"ATT\"][\"line\"]); show(\"T6 SE\", t6[\"ATT\"][\"se_line\"])\n show(\"T6 Obs\", t6[\"Observations\"][\"line\"])\n res[\"dcr_att_5km\"] = num(t6[\"ATT\"][\"est\"][\"5 km\"])\n res[\"dcr_att_5km_se\"] = num(t6[\"ATT\"][\"se\"][\"5 km\"])\n res[\"dcr_att_3km\"] = num(t6[\"ATT\"][\"est\"][\"3 km\"])\n res[\"dcr_att_3km_se\"] = num(t6[\"ATT\"][\"se\"][\"3 km\"])\n res[\"dcr_obs_5km\"] = num(t6[\"Observations\"][\"est\"][\"5 km\"])\n loc[\"dcr_att_5km\"] = L(DCR_ID, 6, \"ATT\", \"5 km\")\n loc[\"dcr_att_5km_se\"] = L(DCR_ID, 6, \"ATT\", \"5 km\", \" (standard error in parentheses, line below)\")\n loc[\"dcr_att_3km\"] = L(DCR_ID, 6, \"ATT\", \"3 km\")\n loc[\"dcr_att_3km_se\"] = L(DCR_ID, 6, \"ATT\", \"3 km\", \" (standard error in parentheses, line below)\")\n loc[\"dcr_obs_5km\"] = L(DCR_ID, 6, \"Observations\", \"5 km\")\n\n # ---------------- DCR Table 7 ----------------\n c7, t7 = reg_table(DL, \"Table 7:\")\n for r in (\"Narcotics\", \"Drug-related (fatal)\", \"HIV\"):\n show(\"T7 \" + r, t7[r][\"line\"]); show(\"T7 SE \" + r, t7[r][\"se_line\"])\n spec = [(\"dcr_narcotics_att_3km\", \"Narcotics\", \"3 km\"),\n (\"dcr_fatal_att_1km\", \"Drug-related (fatal)\", \"1 km\"),\n (\"dcr_hiv_att_5km\", \"HIV\", \"5 km\")]\n for key, r, c in spec:\n res[key] = num(t7[r][\"est\"][c]); loc[key] = L(DCR_ID, 7, r, c)\n res[key + \"_se\"] = num(t7[r][\"se\"][c])\n loc[key + \"_se\"] = L(DCR_ID, 7, r, c, \" (standard error in parentheses, line below)\")\n hiv_cell = t7[\"HIV\"][\"est\"][\"5 km\"]\n # exact p-value: search extract for any printed p-value\n pv_hits = [l for l in DL if re.search(r\"\\bp\\s*=\\s*0?\\.\\d\", l)]\n print(f\"[DCR] lines with an exact 'p=' value in extract: {len(pv_hits)}\")\n res[\"dcr_hiv_att_5km_p_value\"] = None\n loc[\"dcr_hiv_att_5km_p_value\"] = (\"not reported in \" + DCR_ID + \" (Table 7 prints only the estimate, \"\n \"its SE in parentheses and significance stars; no exact p-value is printed)\")\n res[\"dcr_hiv_att_5km_significant_10pct\"] = stars(hiv_cell) >= 1\n\n # ---------------- derived CI (DCR) ----------------\n b, s = res[\"dcr_att_5km\"], res[\"dcr_att_5km_se\"]\n res[\"dcr_att_5km_ci_low\"] = round(b - Z * s, 4)\n res[\"dcr_att_5km_ci_high\"] = round(b + Z * s, 4)\n for k in (\"dcr_att_5km_ci_low\", \"dcr_att_5km_ci_high\"):\n loc[k] = (f'{DCR_ID} | Table 6 | row \"ATT\" | column \"5 km\" | derived from printed ATT '\n f'and SE: ATT {\"-\" if k.endswith(\"low\") else \"+\"} 1.96 x SE')\n\n # ---------------- RESEA Table 2 ----------------\n i2 = next(k for k, l in enumerate(RL) if l.startswith(\"Outcome | Treatment | Control\"))\n hdr2 = cells(RL[i2])\n t2 = {}\n for k in range(i2 + 1, len(RL)):\n if not RL[k].strip(): break\n c = cells(RL[k]); t2[c[0]] = (dict(zip(hdr2[1:], c[1:])), (k + 1, RL[k]))\n for r in (\"Wage\", \"Reemployment\", \"Weeks on UI\"):\n show(\"T2 \" + r, t2[r][1])\n ATE = \"Average treatment effect estimate\"\n\n def ate_parse(cell):\n m = re.match(r\"\\s*(-?\\$?-?[\\d,\\.]+)\\s*(pp|weeks|wks)?;\\s*p\\s*([=<])\\s*([\\d\\.]+);\\s*95% CI:\\s*\\[([^\\]]+)\\]\", cell)\n est = num(m.group(1)); lo, hi = [num(x) for x in m.group(5).split(\", \")]\n return est, m.group(3) + m.group(4), lo, hi\n\n w_est, w_p, w_lo, w_hi = ate_parse(t2[\"Wage\"][0][ATE])\n r_est, r_p, r_lo, r_hi = ate_parse(t2[\"Reemployment\"][0][ATE])\n u_est, u_p, u_lo, u_hi = ate_parse(t2[\"Weeks on UI\"][0][ATE])\n res[\"ri_wage_ate\"], res[\"ri_wage_ate_ci_low\"], res[\"ri_wage_ate_ci_high\"] = w_est, w_lo, w_hi\n res[\"ri_wage_ate_se_reported\"] = bool(re.search(r\"\\bSE\\b|standard error|\\(\\d\", t2[\"Wage\"][0][ATE]))\n res[\"ri_wage_ate_se_derived\"] = round((w_hi - w_lo) / (2 * Z), 2)\n ctrl = re.match(r\"\\$([\\d,]+)\", t2[\"Wage\"][0][\"Control\"]).group(1)\n res[\"ri_control_mean_wage\"] = num(ctrl)\n res[\"ri_reemployment_ate_pp\"] = r_est\n res[\"ri_weeks_ui_ate\"], res[\"ri_weeks_ui_ate_ci_low\"], res[\"ri_weeks_ui_ate_ci_high\"] = u_est, u_lo, u_hi\n loc[\"ri_wage_ate\"] = L(RI_ID, 2, \"Wage\", ATE, \" (point estimate)\")\n loc[\"ri_wage_ate_ci_low\"] = L(RI_ID, 2, \"Wage\", ATE, \" (95% CI, lower bound)\")\n loc[\"ri_wage_ate_ci_high\"] = L(RI_ID, 2, \"Wage\", ATE, \" (95% CI, upper bound)\")\n loc[\"ri_wage_ate_se_derived\"] = L(RI_ID, 2, \"Wage\", ATE,\n \" | derived from printed 95% CI: (upper - lower) / (2 x 1.96)\")\n loc[\"ri_control_mean_wage\"] = L(RI_ID, 2, \"Wage\", \"Control\", \" (mean)\")\n loc[\"ri_reemployment_ate_pp\"] = L(RI_ID, 2, \"Reemployment\", ATE, \" (point estimate)\")\n loc[\"ri_weeks_ui_ate\"] = L(RI_ID, 2, \"Weeks on UI\", ATE, \" (point estimate)\")\n loc[\"ri_weeks_ui_ate_ci_low\"] = L(RI_ID, 2, \"Weeks on UI\", ATE, \" (95% CI, lower bound)\")\n loc[\"ri_weeks_ui_ate_ci_high\"] = L(RI_ID, 2, \"Weeks on UI\", ATE, \" (95% CI, upper bound)\")\n\n # ---------------- consistency checks -> data_issues ----------------\n # (a) Table 6 vs Table A19 SE for the same 5 km CS-DR ATT\n cA, tA = reg_table(DL, \"Table A19:\")\n a19_att, a19_se = num(tA[\"ATT\"][\"est\"][\"CS-DR\"]), num(tA[\"ATT\"][\"se\"][\"CS-DR\"])\n show(\"A19 ATT\", tA[\"ATT\"][\"line\"]); show(\"A19 SE\", tA[\"ATT\"][\"se_line\"])\n if a19_att == res[\"dcr_att_5km\"] and a19_se != res[\"dcr_att_5km_se\"]:\n issues.append({\"issue\": f\"Same 5 km CS-DR ATT ({a19_att}) printed with SE {res['dcr_att_5km_se']} in Table 6 \"\n f\"but SE {a19_se} in Table A19 (CS-DR column) of {DCR_ID}.\",\n \"handling\": \"Used Table 6 (the table named in the key definitions) for dcr_att_5km_se and the derived CI; \"\n \"the significance star (**) is the same under either SE.\"})\n # (b) control-region count: 608 (Tables 6/7, Fig. 6) vs 640 (A19 note); observations arithmetic\n a19_note = next(l for l in DL if \"never-treated comparison regions (\" in l)\n m = re.search(r\"\\((\\d+) regions\\)\", a19_note)\n n_ctrl6 = num(t6[\"Control regions\"][\"est\"][\"5 km\"])\n obs = {c: num(t6[\"Observations\"][\"est\"][c]) for c in c6}\n trt = {c: num(t6[\"Treated regions\"][\"est\"][c]) for c in c6}\n per_reg = {c: obs[c] / (trt[c] + n_ctrl6) for c in c6}\n per_reg640 = {c: obs[c] / (trt[c] + 640) for c in c6}\n print(f\"[check] obs/(treated+608) = {per_reg}; obs/(treated+640) = {per_reg640}\")\n if m and int(m.group(1)) != n_ctrl6:\n issues.append({\"issue\": f\"Control-region count inconsistent within {DCR_ID}: Tables 6/7 and the Figure 6 note say \"\n f\"{n_ctrl6} never-treated regions; the Table A19 note says {m.group(1)}. Table 6 observations \"\n f\"divided by (treated + {n_ctrl6}) are non-integers ({', '.join(f'{v:.2f}' for v in per_reg.values())}), \"\n f\"whereas divided by (treated + 640) they equal {', '.join(f'{v:g}' for v in per_reg640.values())}.\",\n \"handling\": \"Reported dcr_obs_5km exactly as printed in Table 6 (17,250); flagged, not resolved (the panel structure is not stated in the extract).\"})\n # (c) abstract: fatal outcomes 'fall by 35%' vs Table 7 5 km implied change\n fat5 = num(t7[\"Drug-related (fatal)\"][\"est\"][\"5 km\"]); narc5 = num(t7[\"Narcotics\"][\"est\"][\"5 km\"])\n pct = lambda x: 100 * (math.exp(x) - 1)\n print(f\"[check] exp(ATT)-1: fatal 5km {pct(fat5):.1f}%, narcotics 5km {pct(narc5):.1f}%, overall 5km {pct(res['dcr_att_5km']):.1f}%\")\n if any(\"fall by 35%\" in l for l in DL):\n issues.append({\"issue\": f\"Abstract of {DCR_ID} says fatal outcomes 'fall by 35%', but Table 7 fatal 5 km ATT {fat5} implies \"\n f\"{pct(fat5):.1f}% (Section 6.1 says ~30% at 5 km); 35% matches the overall 5 km ATT. The text also calls HIV \"\n f\"estimates 'close to zero' although the 5 km HIV ATT is {res['dcr_hiv_att_5km']} (insignificant).\",\n \"handling\": \"Narrative statements not used; all values taken from the tables named in the keys.\"})\n # (d) RESEA text gives weeks-on-UI CI as positive magnitudes\n if any(\"[1.75 weeks, 2.24 weeks]\" in l for l in RL):\n issues.append({\"issue\": f\"{RI_ID} text reports the weeks-on-UI CI as magnitudes '[1.75 weeks, 2.24 weeks]' (a reduction), \"\n f\"while Table 2 prints [{u_lo}, {u_hi}].\",\n \"handling\": \"Used Table 2 signs and order (lower bound first).\"})\n # (e) rendering defect\n if any(\"p ¡ 0.001\" in l for l in RL):\n issues.append({\"issue\": f\"Rendering defect in {RI_ID} text: 'p ¡ 0.001' (should read p < 0.001).\",\n \"handling\": \"Not used for any key; Table 2 values used.\"})\n # star / SE consistency (not an issue if consistent)\n from statistics import NormalDist\n nd = NormalDist()\n star_ok = []\n for tab, rows, cols in ((\"6\", t6, c6), (\"7\", t7, c7)):\n for r, d in rows.items():\n if \"se\" not in d: continue\n for c in cols:\n e, se_ = num(d[\"est\"][c]), num(d[\"se\"][c])\n p = 2 * (1 - nd.cdf(abs(e / se_)))\n exp_st = 3 if p < .01 else 2 if p < .05 else 1 if p < .10 else 0\n star_ok.append((tab, r, c, round(p, 4), stars(d[\"est\"][c]), exp_st))\n mism = [x for x in star_ok if x[4] != x[5]]\n print(f\"[check] star vs normal-approx p from est/SE: {len(star_ok)} cells, {len(mism)} mismatches {mism}\")\n hiv_p_approx = [x[3] for x in star_ok if x[1] == \"HIV\" and x[2] == \"5 km\"][0]\n # RESEA CI symmetry\n print(f\"[check] wage CI half-widths: {w_est - w_lo}, {w_hi - w_est}; weeks: {u_est - u_lo:.2f}, {u_hi - u_est:.2f}\")\n\n # ---------------- baseline / sensitivity ----------------\n res[\"baseline_dcr_att_5km_ci_rule2\"] = [round(b - 2 * s, 4), round(b + 2 * s, 4)]\n res[\"baseline_ri_wage_ate_se_rule2\"] = round((w_hi - w_lo) / 4, 2)\n res[\"check_dcr_hiv_att_5km_normal_approx_p_not_printed\"] = hiv_p_approx\n\n out = {}\n order = [\"dcr_att_5km\", \"dcr_att_5km_se\", \"dcr_att_3km\", \"dcr_att_3km_se\", \"dcr_obs_5km\",\n \"dcr_narcotics_att_3km\", \"dcr_narcotics_att_3km_se\", \"dcr_fatal_att_1km\", \"dcr_fatal_att_1km_se\",\n \"dcr_hiv_att_5km\", \"dcr_hiv_att_5km_se\", \"dcr_hiv_att_5km_p_value\", \"dcr_hiv_att_5km_significant_10pct\",\n \"dcr_att_5km_ci_low\", \"dcr_att_5km_ci_high\", \"ri_wage_ate\", \"ri_wage_ate_ci_low\", \"ri_wage_ate_ci_high\",\n \"ri_wage_ate_se_reported\", \"ri_wage_ate_se_derived\", \"ri_control_mean_wage\", \"ri_reemployment_ate_pp\",\n \"ri_weeks_ui_ate\", \"ri_weeks_ui_ate_ci_low\", \"ri_weeks_ui_ate_ci_high\"]\n for k in order: out[k] = res[k]\n out[\"locators\"] = {k: loc[k] for k in order if k in loc}\n out[\"derivation_notes\"] = {\n \"dcr_att_5km_ci\": \"Normal-approximation (Wald) 95% CI from Table 6 printed values: ATT +/- 1.96 x SE = \"\n f\"{b} +/- 1.96 x {s}; rounded to 4 decimals. Assumes approx. normal sampling distribution of the \"\n \"CS-DR ATT with the printed clustered SE; a 2.0 rule gives \"\n f\"{res['baseline_dcr_att_5km_ci_rule2']}.\",\n \"ri_wage_ate_se\": \"SE implied by the Table 2 printed 95% CI assuming a symmetric normal (Wald) interval: \"\n f\"(upper - lower) / (2 x 1.96) = ({w_hi} - ({w_lo})) / 3.92; rounded to 2 decimals. With N = 23,549 a \"\n f\"t critical value is practically 1.96; a 2.0 rule gives {res['baseline_ri_wage_ate_se_rule2']}. \"\n \"Printed bounds are rounded to whole dollars, so the derived SE is accurate to about +/-0.2.\"}\n out[\"task_id\"] = \"F4-conf-01\"\n out[\"feasibility\"] = \"complete\"\n out[\"feasibility_reason\"] = None\n out[\"data_issues\"] = issues\n nrows = {}\n for p in (os.path.join(DATA, \"README.md\"), DCR_F, RI_F):\n with open(p, \"rb\") as f: nrows[os.path.relpath(p, DATA)] = f.read().count(b\"\\n\")\n out[\"n_input_rows\"] = nrows\n out[\"n_analysed\"] = len(order)\n out[\"random_seed\"] = None\n out[\"baseline_dcr_att_5km_ci_rule2\"] = res[\"baseline_dcr_att_5km_ci_rule2\"]\n out[\"baseline_ri_wage_ate_se_rule2\"] = res[\"baseline_ri_wage_ate_se_rule2\"]\n out[\"check_dcr_hiv_att_5km_normal_approx_p_not_printed\"] = res[\"check_dcr_hiv_att_5km_normal_approx_p_not_printed\"]\n out[\"input_sha256\"] = {os.path.relpath(p, DATA): hashlib.sha256(open(p, \"rb\").read()).hexdigest()\n for p in (os.path.join(DATA, \"README.md\"), DCR_F, RI_F)}\n os.makedirs(\"out\", exist_ok=True)\n with open(os.path.join(\"out\", \"results.json\"), \"w\") as f:\n json.dump(out, f, indent=2, ensure_ascii=False); f.write(\"\\n\")\n print(\"Wrote out/results.json\")\n\n\nif __name__ == \"__main__\":\n main()\nEOF\n~/wp29/venv/bin/python out/analysis.py && cat out/results.json","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":980,"cache_read_input_tokens":21156,"output_tokens":7873,"output_tokens_details":{"thinking_tokens":274},"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":980},"inference_geo":"global","iterations":[],"speed":"standard","fallback_credit":null},"input_transformations":[],"diagnostics":null,"context_management":null},"apiBlockIndex":1,"requestId":"req_011CgXs4m4EvULRLoQJ7jViV","requestRef":"8be0d2dd-b37c-4fc7-8354-a819f854b786","requestedModel":"claude-opus-5-5","type":"assistant","uuid":"354f39f9-4ea0-4c0d-aba3-961dd3a7c6dd","timestamp":"2026-10-30T05:25:14.505Z","effort":"medium","perTurnEffort":"medium","userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0284","sessionId":"dada2278-6d32-4907-8ce0-67560ca80944","version":"2.1.314","gitBranch":"HEAD"}40{"parentUuid":"354f39f9-4ea0-4c0d-aba3-961dd3a7c6dd","isSidechain":false,"promptId":"52960967-79c0-4a18-8c3d-73f4cb1f259b","type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_01HmtDZBR7i4dvFwJrVWmLGJ","type":"tool_result","content":"[T6 ATT] line 36: ATT | -0.399 | -0.283 | -0.431**\n[T6 SE] line 38: | (0.412) | (0.279) | (0.210)\n[T6 Obs] line 46: Observations | 16,175 | 16,875 | 17,250\n[T7 Narcotics] line 82: Narcotics | 1.186*** | 0.549*** | 0.390**\n[T7 SE Narcotics] line 84: | (0.288) | (0.187) | (0.165)\n[T7 Drug-related (fatal)] line 86: Drug-related (fatal) | -1.207*** | -0.290 | -0.354**\n[T7 SE Drug-related (fatal)] line 88: | (0.137) | (0.215) | (0.178)\n[T7 HIV] line 78: HIV | 0.063 | -0.239 | -0.340\n[T7 SE HIV] line 80: | (0.801) | (0.337) | (0.294)\n[DCR] lines with an exact 'p=' value in extract: 0\n[T2 Wage] line 28: Wage | $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[T2 Reemployment] line 29: Reemployment | 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[T2 Weeks on UI] line 30: Weeks 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[A19 ATT] line 121: ATT | -0.431** | -0.438** | -0.362* | -0.149***\n[A19 SE] line 123: | (0.218) | (0.208) | (0.205) | (0.038)\n[check] obs/(treated+608) = {'1 km': 26.30081300813008, '3 km': 26.24416796267496, '5 km': 26.21580547112462}; obs/(treated+640) = {'1 km': 25.0, '3 km': 25.0, '5 km': 25.0}\n[check] exp(ATT)-1: fatal 5km -29.8%, narcotics 5km 47.7%, overall 5km -35.0%\n[check] star vs normal-approx p from est/SE: 18 cells, 0 mismatches []\n[check] wage CI half-widths: 987, 988; weeks: 0.25, 0.24\nWrote out/results.json\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,\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 \"locators\": {\n \"dcr_att_5km\": \"arxiv-2609.27732v1 | Table 6 | row \\\"ATT\\\" | column \\\"5 km\\\"\",\n \"dcr_att_5km_se\": \"arxiv-2609.27732v1 | Table 6 | row \\\"ATT\\\" | column \\\"5 km\\\" (standard error in parentheses, line below)\",\n \"dcr_att_3km\": \"arxiv-2609.27732v1 | Table 6 | row \\\"ATT\\\" | column \\\"3 km\\\"\",\n \"dcr_att_3km_se\": \"arxiv-2609.27732v1 | Table 6 | row \\\"ATT\\\" | column \\\"3 km\\\" (standard error in parentheses, line below)\",\n \"dcr_obs_5km\": \"arxiv-2609.27732v1 | Table 6 | row \\\"Observations\\\" | column \\\"5 km\\\"\",\n \"dcr_narcotics_att_3km\": \"arxiv-2609.27732v1 | Table 7 | row \\\"Narcotics\\\" | column \\\"3 km\\\"\",\n \"dcr_narcotics_att_3km_se\": \"arxiv-2609.27732v1 | Table 7 | row \\\"Narcotics\\\" | column \\\"3 km\\\" (standard error in parentheses, line below)\",\n \"dcr_fatal_att_1km\": \"arxiv-2609.27732v1 | Table 7 | row \\\"Drug-related (fatal)\\\" | column \\\"1 km\\\"\",\n \"dcr_fatal_att_1km_se\": \"arxiv-2609.27732v1 | Table 7 | row \\\"Drug-related (fatal)\\\" | column \\\"1 km\\\" (standard error in parentheses, line below)\",\n \"dcr_hiv_att_5km\": \"arxiv-2609.27732v1 | Table 7 | row \\\"HIV\\\" | column \\\"5 km\\\"\",\n \"dcr_hiv_att_5km_se\": \"arxiv-2609.27732v1 | Table 7 | row \\\"HIV\\\" | column \\\"5 km\\\" (standard error in parentheses, line below)\",\n \"dcr_hiv_att_5km_p_value\": \"not reported in arxiv-2609.27732v1 (Table 7 prints only the estimate, its SE in parentheses and significance stars; no exact p-value is printed)\",\n \"dcr_att_5km_ci_low\": \"arxiv-2609.27732v1 | Table 6 | row \\\"ATT\\\" | column \\\"5 km\\\" | derived from printed ATT and SE: ATT - 1.96 x SE\",\n \"dcr_att_5km_ci_high\": \"arxiv-2609.27732v1 | Table 6 | row \\\"ATT\\\" | column \\\"5 km\\\" | derived from printed ATT and SE: ATT + 1.96 x SE\",\n \"ri_wage_ate\": \"arxiv-2606.14621v1 | Table 2 | row \\\"Wage\\\" | column \\\"Average treatment effect estimate\\\" (point estimate)\",\n \"ri_wage_ate_ci_low\": \"arxiv-2606.14621v1 | Table 2 | row \\\"Wage\\\" | column \\\"Average treatment effect estimate\\\" (95% CI, lower bound)\",\n \"ri_wage_ate_ci_high\": \"arxiv-2606.14621v1 | Table 2 | row \\\"Wage\\\" | column \\\"Average treatment effect estimate\\\" (95% CI, upper bound)\",\n \"ri_wage_ate_se_derived\": \"arxiv-2606.14621v1 | Table 2 | row \\\"Wage\\\" | column \\\"Average treatment effect estimate\\\" | derived from printed 95% CI: (upper - lower) / (2 x 1.96)\",\n \"ri_control_mean_wage\": \"arxiv-2606.14621v1 | Table 2 | row \\\"Wage\\\" | column \\\"Control\\\" (mean)\",\n \"ri_reemployment_ate_pp\": \"arxiv-2606.14621v1 | Table 2 | row \\\"Reemployment\\\" | column \\\"Average treatment effect estimate\\\" (point estimate)\",\n \"ri_weeks_ui_ate\": \"arxiv-2606.14621v1 | Table 2 | row \\\"Weeks on UI\\\" | column \\\"Average treatment effect estimate\\\" (point estimate)\",\n \"ri_weeks_ui_ate_ci_low\": \"arxiv-2606.14621v1 | Table 2 | row \\\"Weeks on UI\\\" | column \\\"Average treatment effect estimate\\\" (95% CI, lower bound)\",\n \"ri_weeks_ui_ate_ci_high\": \"arxiv-2606.14621v1 | Table 2 | row \\\"Weeks on UI\\\" | column \\\"Average treatment effect estimate\\\" (95% CI, upper bound)\"\n },\n \"derivation_notes\": {\n \"dcr_att_5km_ci\": \"Normal-approximation (Wald) 95% CI from Table 6 printed values: ATT +/- 1.96 x SE = -0.431 +/- 1.96 x 0.21; rounded to 4 decimals. Assumes approx. normal sampling distribution of the CS-DR ATT with the printed clustered SE; a 2.0 rule gives [-0.851, -0.011].\",\n \"ri_wage_ate_se\": \"SE implied by the Table 2 printed 95% CI assuming a symmetric normal (Wald) interval: (upper - lower) / (2 x 1.96) = (2141 - (166)) / 3.92; rounded to 2 decimals. With N = 23,549 a t critical value is practically 1.96; a 2.0 rule gives 493.75. Printed bounds are rounded to whole dollars, so the derived SE is accurate to about +/-0.2.\"\n },\n \"task_id\": \"F4-conf-01\",\n \"feasibility\": \"complete\",\n \"feasibility_reason\": null,\n \"data_issues\": [\n {\n \"issue\": \"Same 5 km CS-DR ATT (-0.431) printed with SE 0.21 in Table 6 but SE 0.218 in Table A19 (CS-DR column) of arxiv-2609.27732v1.\",\n \"handling\": \"Used Table 6 (the table named in the key definitions) for dcr_att_5km_se and the derived CI; the significance star (**) is the same under either SE.\"\n },\n {\n \"issue\": \"Control-region count inconsistent within arxiv-2609.27732v1: Tables 6/7 and the Figure 6 note say 608 never-treated regions; the Table A19 note says 640. Table 6 observations divided by (treated + 608) are non-integers (26.30, 26.24, 26.22), whereas divided by (treated + 640) they equal 25, 25, 25.\",\n \"handling\": \"Reported dcr_obs_5km exactly as printed in Table 6 (17,250); flagged, not resolved (the panel structure is not stated in the extract).\"\n },\n {\n \"issue\": \"Abstract of arxiv-2609.27732v1 says fatal outcomes 'fall by 35%', but Table 7 fatal 5 km ATT -0.354 implies -29.8% (Section 6.1 says ~30% at 5 km); 35% matches the overall 5 km ATT. The text also calls HIV estimates 'close to zero' although the 5 km HIV ATT is -0.34 (insignificant).\",\n \"handling\": \"Narrative statements not used; all values taken from the tables named in the keys.\"\n },\n {\n \"issue\": \"arxiv-2606.14621v1 text reports the weeks-on-UI CI as magnitudes '[1.75 weeks, 2.24 weeks]' (a reduction), while Table 2 prints [-2.24, -1.75].\",\n \"handling\": \"Used Table 2 signs and order (lower bound first).\"\n },\n {\n \"issue\": \"Rendering defect in arxiv-2606.14621v1 text: 'p ¡ 0.001' (should read p < 0.001).\",\n \"handling\": \"Not used for any key; Table 2 values used.\"\n }\n ],\n \"n_input_rows\": {\n \"README.md\": 10,\n \"sources/arxiv-2609.27732v1.txt\": 134,\n \"sources/arxiv-2606.14621v1.txt\": 58\n },\n \"n_analysed\": 25,\n \"random_seed\": null,\n \"baseline_dcr_att_5km_ci_rule2\": [\n -0.851,\n -0.011\n ],\n \"baseline_ri_wage_ate_se_rule2\": 493.75,\n \"check_dcr_hiv_att_5km_normal_approx_p_not_printed\": 0.2475,\n \"input_sha256\": {\n \"README.md\": \"16ebdff389eeaee9508fe81c4edc43690f31f3463c469680c814134086ef9929\",\n \"sources/arxiv-2609.27732v1.txt\": \"ee2d444d2f78f2af2ca2cf69b59a61fcd7ac5e10ea29e8ddcd67917bf07d6a8b\",\n \"sources/arxiv-2606.14621v1.txt\": \"27c463c0a144d9d5a3916dc641f15b6029f6c68b5e574ad9074e7df1aeb0a238\"\n }\n}","is_error":false}]},"uuid":"678b6945-e7d7-4c83-9257-7240c6478600","timestamp":"2026-10-30T05:25:14.584Z","toolUseResult":{"stdout":"[T6 ATT] line 36: ATT | -0.399 | -0.283 | -0.431**\n[T6 SE] line 38: | (0.412) | (0.279) | (0.210)\n[T6 Obs] line 46: Observations | 16,175 | 16,875 | 17,250\n[T7 Narcotics] line 82: Narcotics | 1.186*** | 0.549*** | 0.390**\n[T7 SE Narcotics] line 84: | (0.288) | (0.187) | (0.165)\n[T7 Drug-related (fatal)] line 86: Drug-related (fatal) | -1.207*** | -0.290 | -0.354**\n[T7 SE Drug-related (fatal)] line 88: | (0.137) | (0.215) | (0.178)\n[T7 HIV] line 78: HIV | 0.063 | -0.239 | -0.340\n[T7 SE HIV] line 80: | (0.801) | (0.337) | (0.294)\n[DCR] lines with an exact 'p=' value in extract: 0\n[T2 Wage] line 28: Wage | $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[T2 Reemployment] line 29: Reemployment | 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[T2 Weeks on UI] line 30: Weeks 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[A19 ATT] line 121: ATT | -0.431** | -0.438** | -0.362* | -0.149***\n[A19 SE] line 123: | (0.218) | (0.208) | (0.205) | (0.038)\n[check] obs/(treated+608) = {'1 km': 26.30081300813008, '3 km': 26.24416796267496, '5 km': 26.21580547112462}; obs/(treated+640) = {'1 km': 25.0, '3 km': 25.0, '5 km': 25.0}\n[check] exp(ATT)-1: fatal 5km -29.8%, narcotics 5km 47.7%, overall 5km -35.0%\n[check] star vs normal-approx p from est/SE: 18 cells, 0 mismatches []\n[check] wage CI half-widths: 987, 988; weeks: 0.25, 0.24\nWrote out/results.json\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,\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 \"locators\": {\n \"dcr_att_5km\": \"arxiv-2609.27732v1 | Table 6 | row \\\"ATT\\\" | column \\\"5 km\\\"\",\n \"dcr_att_5km_se\": \"arxiv-2609.27732v1 | Table 6 | row \\\"ATT\\\" | column \\\"5 km\\\" (standard error in parentheses, line below)\",\n \"dcr_att_3km\": \"arxiv-2609.27732v1 | Table 6 | row \\\"ATT\\\" | column \\\"3 km\\\"\",\n \"dcr_att_3km_se\": \"arxiv-2609.27732v1 | Table 6 | row \\\"ATT\\\" | column \\\"3 km\\\" (standard error in parentheses, line below)\",\n \"dcr_obs_5km\": \"arxiv-2609.27732v1 | Table 6 | row \\\"Observations\\\" | column \\\"5 km\\\"\",\n \"dcr_narcotics_att_3km\": \"arxiv-2609.27732v1 | Table 7 | row \\\"Narcotics\\\" | column \\\"3 km\\\"\",\n \"dcr_narcotics_att_3km_se\": \"arxiv-2609.27732v1 | Table 7 | row \\\"Narcotics\\\" | column \\\"3 km\\\" (standard error in parentheses, line below)\",\n \"dcr_fatal_att_1km\": \"arxiv-2609.27732v1 | Table 7 | row \\\"Drug-related (fatal)\\\" | column \\\"1 km\\\"\",\n \"dcr_fatal_att_1km_se\": \"arxiv-2609.27732v1 | Table 7 | row \\\"Drug-related (fatal)\\\" | column \\\"1 km\\\" (standard error in parentheses, line below)\",\n \"dcr_hiv_att_5km\": \"arxiv-2609.27732v1 | Table 7 | row \\\"HIV\\\" | column \\\"5 km\\\"\",\n \"dcr_hiv_att_5km_se\": \"arxiv-2609.27732v1 | Table 7 | row \\\"HIV\\\" | column \\\"5 km\\\" (standard error in parentheses, line below)\",\n \"dcr_hiv_att_5km_p_value\": \"not reported in arxiv-2609.27732v1 (Table 7 prints only the estimate, its SE in parentheses and significance stars; no exact p-value is printed)\",\n \"dcr_att_5km_ci_low\": \"arxiv-2609.27732v1 | Table 6 | row \\\"ATT\\\" | column \\\"5 km\\\" | derived from printed ATT and SE: ATT - 1.96 x SE\",\n \"dcr_att_5km_ci_high\": \"arxiv-2609.27732v1 | Table 6 | row \\\"ATT\\\" | column \\\"5 km\\\" | derived from printed ATT and SE: ATT + 1.96 x SE\",\n \"ri_wage_ate\": \"arxiv-2606.14621v1 | Table 2 | row \\\"Wage\\\" | column \\\"Average treatment effect estimate\\\" (point estimate)\",\n \"ri_wage_ate_ci_low\": \"arxiv-2606.14621v1 | Table 2 | row \\\"Wage\\\" | column \\\"Average treatment effect estimate\\\" (95% CI, lower bound)\",\n \"ri_wage_ate_ci_high\": \"arxiv-2606.14621v1 | Table 2 | row \\\"Wage\\\" | column \\\"Average treatment effect estimate\\\" (95% CI, upper bound)\",\n \"ri_wage_ate_se_derived\": \"arxiv-2606.14621v1 | Table 2 | row \\\"Wage\\\" | column \\\"Average treatment effect estimate\\\" | derived from printed 95% CI: (upper - lower) / (2 x 1.96)\",\n \"ri_control_mean_wage\": \"arxiv-2606.14621v1 | Table 2 | row \\\"Wage\\\" | column \\\"Control\\\" (mean)\",\n \"ri_reemployment_ate_pp\": \"arxiv-2606.14621v1 | Table 2 | row \\\"Reemployment\\\" | column \\\"Average treatment effect estimate\\\" (point estimate)\",\n \"ri_weeks_ui_ate\": \"arxiv-2606.14621v1 | Table 2 | row \\\"Weeks on UI\\\" | column \\\"Average treatment effect estimate\\\" (point estimate)\",\n \"ri_weeks_ui_ate_ci_low\": \"arxiv-2606.14621v1 | Table 2 | row \\\"Weeks on UI\\\" | column \\\"Average treatment effect estimate\\\" (95% CI, lower bound)\",\n \"ri_weeks_ui_ate_ci_high\": \"arxiv-2606.14621v1 | Table 2 | row \\\"Weeks on UI\\\" | column \\\"Average treatment effect estimate\\\" (95% CI, upper bound)\"\n },\n \"derivation_notes\": {\n \"dcr_att_5km_ci\": \"Normal-approximation (Wald) 95% CI from Table 6 printed values: ATT +/- 1.96 x SE = -0.431 +/- 1.96 x 0.21; rounded to 4 decimals. Assumes approx. normal sampling distribution of the CS-DR ATT with the printed clustered SE; a 2.0 rule gives [-0.851, -0.011].\",\n \"ri_wage_ate_se\": \"SE implied by the Table 2 printed 95% CI assuming a symmetric normal (Wald) interval: (upper - lower) / (2 x 1.96) = (2141 - (166)) / 3.92; rounded to 2 decimals. With N = 23,549 a t critical value is practically 1.96; a 2.0 rule gives 493.75. Printed bounds are rounded to whole dollars, so the derived SE is accurate to about +/-0.2.\"\n },\n \"task_id\": \"F4-conf-01\",\n \"feasibility\": \"complete\",\n \"feasibility_reason\": null,\n \"data_issues\": [\n {\n \"issue\": \"Same 5 km CS-DR ATT (-0.431) printed with SE 0.21 in Table 6 but SE 0.218 in Table A19 (CS-DR column) of arxiv-2609.27732v1.\",\n \"handling\": \"Used Table 6 (the table named in the key definitions) for dcr_att_5km_se and the derived CI; the significance star (**) is the same under either SE.\"\n },\n {\n \"issue\": \"Control-region count inconsistent within arxiv-2609.27732v1: Tables 6/7 and the Figure 6 note say 608 never-treated regions; the Table A19 note says 640. Table 6 observations divided by (treated + 608) are non-integers (26.30, 26.24, 26.22), whereas divided by (treated + 640) they equal 25, 25, 25.\",\n \"handling\": \"Reported dcr_obs_5km exactly as printed in Table 6 (17,250); flagged, not resolved (the panel structure is not stated in the extract).\"\n },\n {\n \"issue\": \"Abstract of arxiv-2609.27732v1 says fatal outcomes 'fall by 35%', but Table 7 fatal 5 km ATT -0.354 implies -29.8% (Section 6.1 says ~30% at 5 km); 35% matches the overall 5 km ATT. The text also calls HIV estimates 'close to zero' although the 5 km HIV ATT is -0.34 (insignificant).\",\n \"handling\": \"Narrative statements not used; all values taken from the tables named in the keys.\"\n },\n {\n \"issue\": \"arxiv-2606.14621v1 text reports the weeks-on-UI CI as magnitudes '[1.75 weeks, 2.24 weeks]' (a reduction), while Table 2 prints [-2.24, -1.75].\",\n \"handling\": \"Used Table 2 signs and order (lower bound first).\"\n },\n {\n \"issue\": \"Rendering defect in arxiv-2606.14621v1 text: 'p ¡ 0.001' (should read p < 0.001).\",\n \"handling\": \"Not used for any key; Table 2 values used.\"\n }\n ],\n \"n_input_rows\": {\n \"README.md\": 10,\n \"sources/arxiv-2609.27732v1.txt\": 134,\n \"sources/arxiv-2606.14621v1.txt\": 58\n },\n \"n_analysed\": 25,\n \"random_seed\": null,\n \"baseline_dcr_att_5km_ci_rule2\": [\n -0.851,\n -0.011\n ],\n \"baseline_ri_wage_ate_se_rule2\": 493.75,\n \"check_dcr_hiv_att_5km_normal_approx_p_not_printed\": 0.2475,\n \"input_sha256\": {\n \"README.md\": \"16ebdff389eeaee9508fe81c4edc43690f31f3463c469680c814134086ef9929\",\n \"sources/arxiv-2609.27732v1.txt\": \"ee2d444d2f78f2af2ca2cf69b59a61fcd7ac5e10ea29e8ddcd67917bf07d6a8b\",\n \"sources/arxiv-2606.14621v1.txt\": \"27c463c0a144d9d5a3916dc641f15b6029f6c68b5e574ad9074e7df1aeb0a238\"\n }\n}","stderr":"","interrupted":false,"isImage":false,"noOutputExpected":false},"sandboxed":false,"sourceToolAssistantUUID":"354f39f9-4ea0-4c0d-aba3-961dd3a7c6dd","permissionDecision":{"decision":"accept","source":"config","reasonType":"rule","ruleSource":"cliArg"},"userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0284","sessionId":"dada2278-6d32-4907-8ce0-67560ca80944","version":"2.1.314","gitBranch":"HEAD"}41{"type":"api-request-blob","sessionId":"dada2278-6d32-4907-8ce0-67560ca80944","hash":"8ddbfe51de9eec7f73d69837a3d85c2e255cf0b142e91e9d5caf948299ba9f7d","message":{"role":"assistant","content":[{"type":"thinking","thinking":"","signature":"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"},{"type":"tool_use","id":"toolu_01HmtDZBR7i4dvFwJrVWmLGJ","name":"Bash","input":{"command":"cat > out/analysis.py <<'EOF'\n\"\"\"F4-conf-01: extract printed treatment effects / uncertainty from two arXiv extracts.\n\nReads only data/; writes out/results.json deterministically. No randomness.\nRun from workspace root: ~/wp29/venv/bin/python out/analysis.py\n\"\"\"\nimport json, math, os, re, hashlib\n\nDATA = \"data\"\nDCR_ID, RI_ID = \"arxiv-2609.27732v1\", \"arxiv-2606.14621v1\"\nDCR_F = os.path.join(DATA, \"sources\", DCR_ID + \".txt\")\nRI_F = os.path.join(DATA, \"sources\", RI_ID + \".txt\")\nZ = 1.96 # normal critical value for two-sided 95%\n\n\ndef lines(path):\n with open(path, encoding=\"utf-8\") as f:\n return f.read().split(\"\\n\")\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, $, %, thousands commas, parentheses.\"\"\"\n s = s.replace(\"$\", \"\").replace(\",\", \"\").replace(\"*\", \"\").strip().strip(\"()\")\n return int(s) if re.fullmatch(r\"-?\\d+\", s) else float(s)\n\n\ndef stars(s):\n m = re.search(r\"(\\*+)\\s*$\", s.strip())\n return len(m.group(1)) if m else 0\n\n\ndef table_block(L, title_prefix, end_prefix=\"Note:\"):\n i = next(k for k, l in enumerate(L) if l.startswith(title_prefix))\n j = next(k for k in range(i + 1, len(L)) if L[k].startswith(end_prefix))\n return i, [(k, L[k]) for k in range(i + 1, j) if L[k].strip()]\n\n\ndef reg_table(L, title_prefix):\n \"\"\"Parse a regression table: header, coefficient rows followed by SE rows.\"\"\"\n i, blk = table_block(L, title_prefix)\n hdr = cells(blk[0][1])\n cols = [c for c in hdr if c and c != \"Outcome\"]\n rows, last = {}, None\n for k, l in blk[1:]:\n c = cells(l)\n if c[0] == \"\" and last is not None and c[1].startswith(\"(\"):\n rows[last][\"se\"] = dict(zip(cols, c[1:]))\n rows[last][\"se_line\"] = (k + 1, l)\n else:\n last = c[0]\n rows[last] = {\"est\": dict(zip(cols, c[1:])), \"line\": (k + 1, l)}\n return cols, rows\n\n\ndef show(tag, ln):\n print(f\"[{tag}] line {ln[0]}: {ln[1]}\")\n\n\ndef main():\n DL, RL = lines(DCR_F), lines(RI_F)\n res, loc, issues = {}, {}, []\n\n def L(src, t, row, col, extra=\"\"):\n return f'{src} | Table {t} | row \"{row}\" | column \"{col}\"' + extra\n\n # ---------------- DCR Table 6 ----------------\n c6, t6 = reg_table(DL, \"Table 6:\")\n show(\"T6 ATT\", t6[\"ATT\"][\"line\"]); show(\"T6 SE\", t6[\"ATT\"][\"se_line\"])\n show(\"T6 Obs\", t6[\"Observations\"][\"line\"])\n res[\"dcr_att_5km\"] = num(t6[\"ATT\"][\"est\"][\"5 km\"])\n res[\"dcr_att_5km_se\"] = num(t6[\"ATT\"][\"se\"][\"5 km\"])\n res[\"dcr_att_3km\"] = num(t6[\"ATT\"][\"est\"][\"3 km\"])\n res[\"dcr_att_3km_se\"] = num(t6[\"ATT\"][\"se\"][\"3 km\"])\n res[\"dcr_obs_5km\"] = num(t6[\"Observations\"][\"est\"][\"5 km\"])\n loc[\"dcr_att_5km\"] = L(DCR_ID, 6, \"ATT\", \"5 km\")\n loc[\"dcr_att_5km_se\"] = L(DCR_ID, 6, \"ATT\", \"5 km\", \" (standard error in parentheses, line below)\")\n loc[\"dcr_att_3km\"] = L(DCR_ID, 6, \"ATT\", \"3 km\")\n loc[\"dcr_att_3km_se\"] = L(DCR_ID, 6, \"ATT\", \"3 km\", \" (standard error in parentheses, line below)\")\n loc[\"dcr_obs_5km\"] = L(DCR_ID, 6, \"Observations\", \"5 km\")\n\n # ---------------- DCR Table 7 ----------------\n c7, t7 = reg_table(DL, \"Table 7:\")\n for r in (\"Narcotics\", \"Drug-related (fatal)\", \"HIV\"):\n show(\"T7 \" + r, t7[r][\"line\"]); show(\"T7 SE \" + r, t7[r][\"se_line\"])\n spec = [(\"dcr_narcotics_att_3km\", \"Narcotics\", \"3 km\"),\n (\"dcr_fatal_att_1km\", \"Drug-related (fatal)\", \"1 km\"),\n (\"dcr_hiv_att_5km\", \"HIV\", \"5 km\")]\n for key, r, c in spec:\n res[key] = num(t7[r][\"est\"][c]); loc[key] = L(DCR_ID, 7, r, c)\n res[key + \"_se\"] = num(t7[r][\"se\"][c])\n loc[key + \"_se\"] = L(DCR_ID, 7, r, c, \" (standard error in parentheses, line below)\")\n hiv_cell = t7[\"HIV\"][\"est\"][\"5 km\"]\n # exact p-value: search extract for any printed p-value\n pv_hits = [l for l in DL if re.search(r\"\\bp\\s*=\\s*0?\\.\\d\", l)]\n print(f\"[DCR] lines with an exact 'p=' value in extract: {len(pv_hits)}\")\n res[\"dcr_hiv_att_5km_p_value\"] = None\n loc[\"dcr_hiv_att_5km_p_value\"] = (\"not reported in \" + DCR_ID + \" (Table 7 prints only the estimate, \"\n \"its SE in parentheses and significance stars; no exact p-value is printed)\")\n res[\"dcr_hiv_att_5km_significant_10pct\"] = stars(hiv_cell) >= 1\n\n # ---------------- derived CI (DCR) ----------------\n b, s = res[\"dcr_att_5km\"], res[\"dcr_att_5km_se\"]\n res[\"dcr_att_5km_ci_low\"] = round(b - Z * s, 4)\n res[\"dcr_att_5km_ci_high\"] = round(b + Z * s, 4)\n for k in (\"dcr_att_5km_ci_low\", \"dcr_att_5km_ci_high\"):\n loc[k] = (f'{DCR_ID} | Table 6 | row \"ATT\" | column \"5 km\" | derived from printed ATT '\n f'and SE: ATT {\"-\" if k.endswith(\"low\") else \"+\"} 1.96 x SE')\n\n # ---------------- RESEA Table 2 ----------------\n i2 = next(k for k, l in enumerate(RL) if l.startswith(\"Outcome | Treatment | Control\"))\n hdr2 = cells(RL[i2])\n t2 = {}\n for k in range(i2 + 1, len(RL)):\n if not RL[k].strip(): break\n c = cells(RL[k]); t2[c[0]] = (dict(zip(hdr2[1:], c[1:])), (k + 1, RL[k]))\n for r in (\"Wage\", \"Reemployment\", \"Weeks on UI\"):\n show(\"T2 \" + r, t2[r][1])\n ATE = \"Average treatment effect estimate\"\n\n def ate_parse(cell):\n m = re.match(r\"\\s*(-?\\$?-?[\\d,\\.]+)\\s*(pp|weeks|wks)?;\\s*p\\s*([=<])\\s*([\\d\\.]+);\\s*95% CI:\\s*\\[([^\\]]+)\\]\", cell)\n est = num(m.group(1)); lo, hi = [num(x) for x in m.group(5).split(\", \")]\n return est, m.group(3) + m.group(4), lo, hi\n\n w_est, w_p, w_lo, w_hi = ate_parse(t2[\"Wage\"][0][ATE])\n r_est, r_p, r_lo, r_hi = ate_parse(t2[\"Reemployment\"][0][ATE])\n u_est, u_p, u_lo, u_hi = ate_parse(t2[\"Weeks on UI\"][0][ATE])\n res[\"ri_wage_ate\"], res[\"ri_wage_ate_ci_low\"], res[\"ri_wage_ate_ci_high\"] = w_est, w_lo, w_hi\n res[\"ri_wage_ate_se_reported\"] = bool(re.search(r\"\\bSE\\b|standard error|\\(\\d\", t2[\"Wage\"][0][ATE]))\n res[\"ri_wage_ate_se_derived\"] = round((w_hi - w_lo) / (2 * Z), 2)\n ctrl = re.match(r\"\\$([\\d,]+)\", t2[\"Wage\"][0][\"Control\"]).group(1)\n res[\"ri_control_mean_wage\"] = num(ctrl)\n res[\"ri_reemployment_ate_pp\"] = r_est\n res[\"ri_weeks_ui_ate\"], res[\"ri_weeks_ui_ate_ci_low\"], res[\"ri_weeks_ui_ate_ci_high\"] = u_est, u_lo, u_hi\n loc[\"ri_wage_ate\"] = L(RI_ID, 2, \"Wage\", ATE, \" (point estimate)\")\n loc[\"ri_wage_ate_ci_low\"] = L(RI_ID, 2, \"Wage\", ATE, \" (95% CI, lower bound)\")\n loc[\"ri_wage_ate_ci_high\"] = L(RI_ID, 2, \"Wage\", ATE, \" (95% CI, upper bound)\")\n loc[\"ri_wage_ate_se_derived\"] = L(RI_ID, 2, \"Wage\", ATE,\n \" | derived from printed 95% CI: (upper - lower) / (2 x 1.96)\")\n loc[\"ri_control_mean_wage\"] = L(RI_ID, 2, \"Wage\", \"Control\", \" (mean)\")\n loc[\"ri_reemployment_ate_pp\"] = L(RI_ID, 2, \"Reemployment\", ATE, \" (point estimate)\")\n loc[\"ri_weeks_ui_ate\"] = L(RI_ID, 2, \"Weeks on UI\", ATE, \" (point estimate)\")\n loc[\"ri_weeks_ui_ate_ci_low\"] = L(RI_ID, 2, \"Weeks on UI\", ATE, \" (95% CI, lower bound)\")\n loc[\"ri_weeks_ui_ate_ci_high\"] = L(RI_ID, 2, \"Weeks on UI\", ATE, \" (95% CI, upper bound)\")\n\n # ---------------- consistency checks -> data_issues ----------------\n # (a) Table 6 vs Table A19 SE for the same 5 km CS-DR ATT\n cA, tA = reg_table(DL, \"Table A19:\")\n a19_att, a19_se = num(tA[\"ATT\"][\"est\"][\"CS-DR\"]), num(tA[\"ATT\"][\"se\"][\"CS-DR\"])\n show(\"A19 ATT\", tA[\"ATT\"][\"line\"]); show(\"A19 SE\", tA[\"ATT\"][\"se_line\"])\n if a19_att == res[\"dcr_att_5km\"] and a19_se != res[\"dcr_att_5km_se\"]:\n issues.append({\"issue\": f\"Same 5 km CS-DR ATT ({a19_att}) printed with SE {res['dcr_att_5km_se']} in Table 6 \"\n f\"but SE {a19_se} in Table A19 (CS-DR column) of {DCR_ID}.\",\n \"handling\": \"Used Table 6 (the table named in the key definitions) for dcr_att_5km_se and the derived CI; \"\n \"the significance star (**) is the same under either SE.\"})\n # (b) control-region count: 608 (Tables 6/7, Fig. 6) vs 640 (A19 note); observations arithmetic\n a19_note = next(l for l in DL if \"never-treated comparison regions (\" in l)\n m = re.search(r\"\\((\\d+) regions\\)\", a19_note)\n n_ctrl6 = num(t6[\"Control regions\"][\"est\"][\"5 km\"])\n obs = {c: num(t6[\"Observations\"][\"est\"][c]) for c in c6}\n trt = {c: num(t6[\"Treated regions\"][\"est\"][c]) for c in c6}\n per_reg = {c: obs[c] / (trt[c] + n_ctrl6) for c in c6}\n per_reg640 = {c: obs[c] / (trt[c] + 640) for c in c6}\n print(f\"[check] obs/(treated+608) = {per_reg}; obs/(treated+640) = {per_reg640}\")\n if m and int(m.group(1)) != n_ctrl6:\n issues.append({\"issue\": f\"Control-region count inconsistent within {DCR_ID}: Tables 6/7 and the Figure 6 note say \"\n f\"{n_ctrl6} never-treated regions; the Table A19 note says {m.group(1)}. Table 6 observations \"\n f\"divided by (treated + {n_ctrl6}) are non-integers ({', '.join(f'{v:.2f}' for v in per_reg.values())}), \"\n f\"whereas divided by (treated + 640) they equal {', '.join(f'{v:g}' for v in per_reg640.values())}.\",\n \"handling\": \"Reported dcr_obs_5km exactly as printed in Table 6 (17,250); flagged, not resolved (the panel structure is not stated in the extract).\"})\n # (c) abstract: fatal outcomes 'fall by 35%' vs Table 7 5 km implied change\n fat5 = num(t7[\"Drug-related (fatal)\"][\"est\"][\"5 km\"]); narc5 = num(t7[\"Narcotics\"][\"est\"][\"5 km\"])\n pct = lambda x: 100 * (math.exp(x) - 1)\n print(f\"[check] exp(ATT)-1: fatal 5km {pct(fat5):.1f}%, narcotics 5km {pct(narc5):.1f}%, overall 5km {pct(res['dcr_att_5km']):.1f}%\")\n if any(\"fall by 35%\" in l for l in DL):\n issues.append({\"issue\": f\"Abstract of {DCR_ID} says fatal outcomes 'fall by 35%', but Table 7 fatal 5 km ATT {fat5} implies \"\n f\"{pct(fat5):.1f}% (Section 6.1 says ~30% at 5 km); 35% matches the overall 5 km ATT. The text also calls HIV \"\n f\"estimates 'close to zero' although the 5 km HIV ATT is {res['dcr_hiv_att_5km']} (insignificant).\",\n \"handling\": \"Narrative statements not used; all values taken from the tables named in the keys.\"})\n # (d) RESEA text gives weeks-on-UI CI as positive magnitudes\n if any(\"[1.75 weeks, 2.24 weeks]\" in l for l in RL):\n issues.append({\"issue\": f\"{RI_ID} text reports the weeks-on-UI CI as magnitudes '[1.75 weeks, 2.24 weeks]' (a reduction), \"\n f\"while Table 2 prints [{u_lo}, {u_hi}].\",\n \"handling\": \"Used Table 2 signs and order (lower bound first).\"})\n # (e) rendering defect\n if any(\"p ¡ 0.001\" in l for l in RL):\n issues.append({\"issue\": f\"Rendering defect in {RI_ID} text: 'p ¡ 0.001' (should read p < 0.001).\",\n \"handling\": \"Not used for any key; Table 2 values used.\"})\n # star / SE consistency (not an issue if consistent)\n from statistics import NormalDist\n nd = NormalDist()\n star_ok = []\n for tab, rows, cols in ((\"6\", t6, c6), (\"7\", t7, c7)):\n for r, d in rows.items():\n if \"se\" not in d: continue\n for c in cols:\n e, se_ = num(d[\"est\"][c]), num(d[\"se\"][c])\n p = 2 * (1 - nd.cdf(abs(e / se_)))\n exp_st = 3 if p < .01 else 2 if p < .05 else 1 if p < .10 else 0\n star_ok.append((tab, r, c, round(p, 4), stars(d[\"est\"][c]), exp_st))\n mism = [x for x in star_ok if x[4] != x[5]]\n print(f\"[check] star vs normal-approx p from est/SE: {len(star_ok)} cells, {len(mism)} mismatches {mism}\")\n hiv_p_approx = [x[3] for x in star_ok if x[1] == \"HIV\" and x[2] == \"5 km\"][0]\n # RESEA CI symmetry\n print(f\"[check] wage CI half-widths: {w_est - w_lo}, {w_hi - w_est}; weeks: {u_est - u_lo:.2f}, {u_hi - u_est:.2f}\")\n\n # ---------------- baseline / sensitivity ----------------\n res[\"baseline_dcr_att_5km_ci_rule2\"] = [round(b - 2 * s, 4), round(b + 2 * s, 4)]\n res[\"baseline_ri_wage_ate_se_rule2\"] = round((w_hi - w_lo) / 4, 2)\n res[\"check_dcr_hiv_att_5km_normal_approx_p_not_printed\"] = hiv_p_approx\n\n out = {}\n order = [\"dcr_att_5km\", \"dcr_att_5km_se\", \"dcr_att_3km\", \"dcr_att_3km_se\", \"dcr_obs_5km\",\n \"dcr_narcotics_att_3km\", \"dcr_narcotics_att_3km_se\", \"dcr_fatal_att_1km\", \"dcr_fatal_att_1km_se\",\n \"dcr_hiv_att_5km\", \"dcr_hiv_att_5km_se\", \"dcr_hiv_att_5km_p_value\", \"dcr_hiv_att_5km_significant_10pct\",\n \"dcr_att_5km_ci_low\", \"dcr_att_5km_ci_high\", \"ri_wage_ate\", \"ri_wage_ate_ci_low\", \"ri_wage_ate_ci_high\",\n \"ri_wage_ate_se_reported\", \"ri_wage_ate_se_derived\", \"ri_control_mean_wage\", \"ri_reemployment_ate_pp\",\n \"ri_weeks_ui_ate\", \"ri_weeks_ui_ate_ci_low\", \"ri_weeks_ui_ate_ci_high\"]\n for k in order: out[k] = res[k]\n out[\"locators\"] = {k: loc[k] for k in order if k in loc}\n out[\"derivation_notes\"] = {\n \"dcr_att_5km_ci\": \"Normal-approximation (Wald) 95% CI from Table 6 printed values: ATT +/- 1.96 x SE = \"\n f\"{b} +/- 1.96 x {s}; rounded to 4 decimals. Assumes approx. normal sampling distribution of the \"\n \"CS-DR ATT with the printed clustered SE; a 2.0 rule gives \"\n f\"{res['baseline_dcr_att_5km_ci_rule2']}.\",\n \"ri_wage_ate_se\": \"SE implied by the Table 2 printed 95% CI assuming a symmetric normal (Wald) interval: \"\n f\"(upper - lower) / (2 x 1.96) = ({w_hi} - ({w_lo})) / 3.92; rounded to 2 decimals. With N = 23,549 a \"\n f\"t critical value is practically 1.96; a 2.0 rule gives {res['baseline_ri_wage_ate_se_rule2']}. \"\n \"Printed bounds are rounded to whole dollars, so the derived SE is accurate to about +/-0.2.\"}\n out[\"task_id\"] = \"F4-conf-01\"\n out[\"feasibility\"] = \"complete\"\n out[\"feasibility_reason\"] = None\n out[\"data_issues\"] = issues\n nrows = {}\n for p in (os.path.join(DATA, \"README.md\"), DCR_F, RI_F):\n with open(p, \"rb\") as f: nrows[os.path.relpath(p, DATA)] = f.read().count(b\"\\n\")\n out[\"n_input_rows\"] = nrows\n out[\"n_analysed\"] = len(order)\n out[\"random_seed\"] = None\n out[\"baseline_dcr_att_5km_ci_rule2\"] = res[\"baseline_dcr_att_5km_ci_rule2\"]\n out[\"baseline_ri_wage_ate_se_rule2\"] = res[\"baseline_ri_wage_ate_se_rule2\"]\n out[\"check_dcr_hiv_att_5km_normal_approx_p_not_printed\"] = res[\"check_dcr_hiv_att_5km_normal_approx_p_not_printed\"]\n out[\"input_sha256\"] = {os.path.relpath(p, DATA): hashlib.sha256(open(p, \"rb\").read()).hexdigest()\n for p in (os.path.join(DATA, \"README.md\"), DCR_F, RI_F)}\n os.makedirs(\"out\", exist_ok=True)\n with open(os.path.join(\"out\", \"results.json\"), \"w\") as f:\n json.dump(out, f, indent=2, ensure_ascii=False); f.write(\"\\n\")\n print(\"Wrote out/results.json\")\n\n\nif __name__ == \"__main__\":\n main()\nEOF\n~/wp29/venv/bin/python out/analysis.py && cat out/results.json","description":"Write and run analysis 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\"dcr_att_5km_se\": 0.21,\n \"dcr_att_5km_ci_low\": \"arxiv-2609.27732v1 | Table 6 | row \\\"ATT\\\" | column \\\"5 km\\\" | derived from printed ATT and SE: ATT - 1.96 x SE\",\n \"dcr_att_5km_ci_high\": \"arxiv-2609.27732v1 | Table 6 | row \\\"ATT\\\" | column \\\"5 km\\\" | derived from printed ATT and SE: ATT + 1.96 x SE\",\n \"dcr_att_5km_ci\": \"Normal-approximation (Wald) 95% CI from Table 6 printed values: ATT +/- 1.96 x SE = -0.431 +/- 1.96 x 0.210; rounded to 4 decimals. Assumes approx. normal sampling distribution \n \"issue\": \"Same 5 km CS-DR ATT (-0.431) printed with SE (0.210) in Table 6 but SE (0.218) in Table A19 (CS-DR column) of arxiv-2609.27732v1.\",\n \"issue\": \"Abstract of arxiv-2609.27732v1 says fatal outcomes 'fall by 35%', but Table 7 fatal 5 km ATT -0.354 implies -29.8% (Section 6.1 says ~30% at 5 km); 35% matches the overall 5 km ATT. Th","is_error":false}]},"uuid":"1ead2283-c959-41d2-8d84-ab211883ee54","timestamp":"2026-10-30T05:25:22.806Z","toolUseResult":{"stdout":" \"dcr_att_5km_se\": 0.21,\n \"dcr_att_5km_ci_low\": \"arxiv-2609.27732v1 | Table 6 | row \\\"ATT\\\" | column \\\"5 km\\\" | derived from printed ATT and SE: ATT - 1.96 x SE\",\n \"dcr_att_5km_ci_high\": \"arxiv-2609.27732v1 | Table 6 | row \\\"ATT\\\" | column \\\"5 km\\\" | derived from printed ATT and SE: ATT + 1.96 x SE\",\n \"dcr_att_5km_ci\": \"Normal-approximation (Wald) 95% CI from Table 6 printed values: ATT +/- 1.96 x SE = -0.431 +/- 1.96 x 0.210; rounded to 4 decimals. Assumes approx. normal sampling distribution \n \"issue\": \"Same 5 km CS-DR ATT (-0.431) printed with SE (0.210) in Table 6 but SE (0.218) in Table A19 (CS-DR column) of arxiv-2609.27732v1.\",\n \"issue\": \"Abstract of arxiv-2609.27732v1 says fatal outcomes 'fall by 35%', but Table 7 fatal 5 km ATT -0.354 implies -29.8% (Section 6.1 says ~30% at 5 km); 35% matches the overall 5 km ATT. 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the audit, exclusions, and 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> out/state/data_audit.md <<'EOF'\n# Data audit (P1)\n\n| file | SHA-256 | lines (wc -l) | matches README/bundle hash |\n|---|---|---|---|\n| data/README.md | 16ebdff389eeaee9508fe81c4edc43690f31f3463c469680c814134086ef9929 | 10 | yes (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, UTF-8, LF line endings (0 CR characters). Tables rendered one row per line, cells separated by \" | \".\n- DCR (arxiv-2609.27732v1): Table 6 (3 columns 1/3/5 km; rows ATT + SE line, % change, Treated regions, Control regions, Observations), Table 7 (5 outcome rows each with SE line; 3 buffer columns), Table A19 (4 estimators, 5 km). Units: log points (ATT), region-years (Observations; unit not printed, per TASK). Sample period 1998–2022.\n- RESEA (arxiv-2606.14621v1): Table 2 (6 outcomes × Treatment, Control, T-C difference, ATE; ATE cells hold \"estimate; p; 95% CI: [lo, hi]\"), Table 3 (3 outcomes × 3 estimators). Units: USD/yr, %, pp, weeks. Trial Feb 2022–Sep 2023, N = 23,549.\n- Missing values: none in the requested cells. Table 7 \"Control regions\" row has empty 3/5 km cells (merged cell). Duplicates: Table 3 col 1 duplicates Table 2 ATE column (by design); Table A19 CS-DR duplicates Table 6 5 km ATT but with a different SE (see anomalies).\n\nAnomalies (handled in results.json `data_issues`):\n1. Table 6 SE (0.210) vs Table A19 CS-DR SE (0.218) for the same ATT −0.431 → use Table 6 as the key names it.\n2. Control regions 608 (Tables 6/7, Fig. 6 note) vs 640 (A19 note); Observations / (treated + 640) = 25 exactly for all buffers, not with 608 → flagged; Observations reported as printed.\n3. Abstract \"fatal … fall by 35%\" vs Table 7 5 km fatal −0.354 (≈ −29.8%); HIV called \"close to zero\" though −0.340 at 5 km → narrative not used.\n4. RESEA text weeks CI as positive magnitudes vs Table 2 negative bounds → Table 2 used.\n5. Rendering defect \"p ¡ 0.001\" in RESEA text → not used.\n\nInformation-availability: no input violates the rules; both extracts are the admissible evidence. No exact p-value is printed anywhere in the DCR extract (search for \"p=\" returns 0 lines) → dcr_hiv_att_5km_p_value = null.\nEOF\ncat > out/exclusions.csv <<'EOF'\nstep,rule,n_before,n_after,n_removed,rationale\n1,\"Requested items (TASK.md results keys, excl. metadata) located in the named tables\",25,25,0,\"All 25 requested items were assessed; one (dcr_hiv_att_5km_p_value) is not printed and is reported as null rather than excluded\"\nEOF\necho \"- 2026-10-30 P1 passed: audit written, hashes match bundle.json; 5 data issues logged.\" >> out/state/log.md\necho \"- 2026-10-30 P2 passed: baseline = direct lookup + 2.0-rule derivations (baseline_* keys).\" >> out/state/log.md\necho \"- 2026-10-30 P3 passed: analysis.py parses tables, writes results.json.\" >> 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 /tmp/r1.json out/results.json && echo IDENTICAL\n~/wp29/venv/bin/python - <<'EOF'\nimport json; r=json.load(open('out/results.json')); g=json.load(open('gate_spec.json'))\nprint([k for k in g['results_keys'] if k not in r]); print({k:v for k,v in g.items() if k!='results_keys'})\nprint(r['dcr_att_5km_ci_low']<r['dcr_att_5km']<r['dcr_att_5km_ci_high'], r['ri_wage_ate_ci_low']<r['ri_wage_ate']<r['ri_wage_ate_ci_high'], r['ri_weeks_ui_ate_ci_low']<r['ri_weeks_ui_ate']<r['ri_weeks_ui_ate_ci_high'])\nneed=[k for k in list(r)[:25] if k not in ('dcr_hiv_att_5km_significant_10pct','ri_wage_ate_se_reported')]\nprint('missing locators',[k for k in need if k not in r['locators']])\nEOF","description":"Write audit/exclusions, rerun and verify determinism and gate 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'F4-conf-01', 'required_files': ['out/results.json', 'out/report.md', 'out/analysis.py', 'out/claims.json'], 'report_sections': ['Question', 'Data and exclusions', 'Method', 'Results', 'Uncertainty', 'Limitations and open issues'], 'family_checks': ['claims_reference_existing_keys', 'phase_ledger_consistent'], 'ci_pairs': [], 'positive_keys': [], 'chronology_pairs': [], 'analysis_timeout_s': 300, 'reproducibility_tolerance_rel': 1e-06}\nTrue True True\nmissing locators []","is_error":false}]},"uuid":"43c6450e-a861-47f3-8ce3-7770b714eeed","timestamp":"2026-10-30T05:25:42.140Z","toolUseResult":{"stdout":"IDENTICAL\n[]\n{'task_id': 'F4-conf-01', 'required_files': ['out/results.json', 'out/report.md', 'out/analysis.py', 'out/claims.json'], 'report_sections': ['Question', 'Data and exclusions', 'Method', 'Results', 'Uncertainty', 'Limitations and open issues'], 'family_checks': ['claims_reference_existing_keys', 'phase_ledger_consistent'], 'ci_pairs': [], 'positive_keys': [], 'chronology_pairs': [], 'analysis_timeout_s': 300, 'reproducibility_tolerance_rel': 1e-06}\nTrue True True\nmissing locators []","stderr":"","interrupted":false,"isImage":false,"noOutputExpected":false},"sandboxed":false,"sourceToolAssistantUUID":"b1d6697f-f8a7-4a1f-a85e-563757bba259","permissionDecision":{"decision":"accept","source":"config","reasonType":"rule","ruleSource":"cliArg"},"userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0284","sessionId":"dada2278-6d32-4907-8ce0-67560ca80944","version":"2.1.314","gitBranch":"HEAD"}54{"type":"api-request-blob","sessionId":"dada2278-6d32-4907-8ce0-67560ca80944","hash":"3a3a8a05605ed8f64da6c19685cf3fa62bd424fa004160d56eed259c0203ed10","message":{"role":"assistant","content":[{"type":"thinking","thinking":"","signature":"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"},{"type":"text","text":"Now the audit, exclusions, and verification."},{"type":"tool_use","id":"toolu_01XkFWXaXq2b1wRniEikpQ76","name":"Bash","input":{"command":"cat > out/state/data_audit.md <<'EOF'\n# Data audit (P1)\n\n| file | SHA-256 | lines (wc -l) | matches README/bundle hash |\n|---|---|---|---|\n| data/README.md | 16ebdff389eeaee9508fe81c4edc43690f31f3463c469680c814134086ef9929 | 10 | yes (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, UTF-8, LF line endings (0 CR characters). Tables rendered one row per line, cells separated by \" | \".\n- DCR (arxiv-2609.27732v1): Table 6 (3 columns 1/3/5 km; rows ATT + SE line, % change, Treated regions, Control regions, Observations), Table 7 (5 outcome rows each with SE line; 3 buffer columns), Table A19 (4 estimators, 5 km). Units: log points (ATT), region-years (Observations; unit not printed, per TASK). Sample period 1998–2022.\n- RESEA (arxiv-2606.14621v1): Table 2 (6 outcomes × Treatment, Control, T-C difference, ATE; ATE cells hold \"estimate; p; 95% CI: [lo, hi]\"), Table 3 (3 outcomes × 3 estimators). Units: USD/yr, %, pp, weeks. Trial Feb 2022–Sep 2023, N = 23,549.\n- Missing values: none in the requested cells. Table 7 \"Control regions\" row has empty 3/5 km cells (merged cell). Duplicates: Table 3 col 1 duplicates Table 2 ATE column (by design); Table A19 CS-DR duplicates Table 6 5 km ATT but with a different SE (see anomalies).\n\nAnomalies (handled in results.json `data_issues`):\n1. Table 6 SE (0.210) vs Table A19 CS-DR SE (0.218) for the same ATT −0.431 → use Table 6 as the key names it.\n2. Control regions 608 (Tables 6/7, Fig. 6 note) vs 640 (A19 note); Observations / (treated + 640) = 25 exactly for all buffers, not with 608 → flagged; Observations reported as printed.\n3. Abstract \"fatal … fall by 35%\" vs Table 7 5 km fatal −0.354 (≈ −29.8%); HIV called \"close to zero\" though −0.340 at 5 km → narrative not used.\n4. RESEA text weeks CI as positive magnitudes vs Table 2 negative bounds → Table 2 used.\n5. Rendering defect \"p ¡ 0.001\" in RESEA text → not used.\n\nInformation-availability: no input violates the rules; both extracts are the admissible evidence. No exact p-value is printed anywhere in the DCR extract (search for \"p=\" returns 0 lines) → dcr_hiv_att_5km_p_value = null.\nEOF\ncat > out/exclusions.csv <<'EOF'\nstep,rule,n_before,n_after,n_removed,rationale\n1,\"Requested items (TASK.md results keys, excl. metadata) located in the named tables\",25,25,0,\"All 25 requested items were assessed; one (dcr_hiv_att_5km_p_value) is not printed and is reported as null rather than excluded\"\nEOF\necho \"- 2026-10-30 P1 passed: audit written, hashes match bundle.json; 5 data issues logged.\" >> out/state/log.md\necho \"- 2026-10-30 P2 passed: baseline = direct lookup + 2.0-rule derivations (baseline_* keys).\" >> out/state/log.md\necho \"- 2026-10-30 P3 passed: analysis.py parses tables, writes results.json.\" >> 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 /tmp/r1.json out/results.json && echo IDENTICAL\n~/wp29/venv/bin/python - <<'EOF'\nimport json; r=json.load(open('out/results.json')); g=json.load(open('gate_spec.json'))\nprint([k for k in g['results_keys'] if k not in r]); print({k:v for k,v in g.items() if k!='results_keys'})\nprint(r['dcr_att_5km_ci_low']<r['dcr_att_5km']<r['dcr_att_5km_ci_high'], r['ri_wage_ate_ci_low']<r['ri_wage_ate']<r['ri_wage_ate_ci_high'], r['ri_weeks_ui_ate_ci_low']<r['ri_weeks_ui_ate']<r['ri_weeks_ui_ate_ci_high'])\nneed=[k for k in list(r)[:25] if k not in ('dcr_hiv_att_5km_significant_10pct','ri_wage_ate_se_reported')]\nprint('missing locators',[k for k in need if k not in r['locators']])\nEOF","description":"Write audit/exclusions, rerun and verify determinism and gate keys"}}]}}55{"type":"api-request","sessionId":"dada2278-6d32-4907-8ce0-67560ca80944","id":"f84abb3d-fceb-4117-8147-1be6a23a8d86","timestamp":"2026-10-30T05:25:42.143Z","version":"2.1.314","querySource":"sdk","shapeHash":"6e2648af828e8ce4652445d86f372b634cebc8534a8052871075924b6682d1b1","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":"348b8f12-a8b9-4b47-89f1-100c4054d2c9","keep":14,"tail":["3a3a8a05605ed8f64da6c19685cf3fa62bd424fa004160d56eed259c0203ed10","b94e518e769515c8b4f99ace259ed29c3fe6308c4a4ac11cd6eea4e411c6ee40"],"cacheControl":[{"path":["system",0],"value":{"type":"ephemeral"}},{"path":["system",1],"value":{"type":"ephemeral"}},{"path":["messages",15,"content",0],"value":{"type":"ephemeral"}}],"requestDigest":"17505e8b2f0c8013a2102c97b50afea61a37d68dc5c5bd2ed74f60829fb71e1f"}56{"parentUuid":"43c6450e-a861-47f3-8ce3-7770b714eeed","isSidechain":false,"message":{"model":"claude-opus-5-5","id":"msg_011CgXsBE6cC3Vj9akmKY8PZ","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":2301,"cache_read_input_tokens":35868,"output_tokens":5197,"output_tokens_details":{"thinking_tokens":52},"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":2301},"inference_geo":"global","iterations":[],"speed":"standard","fallback_credit":null},"input_transformations":[],"diagnostics":null,"context_management":null},"thinkingDurationMs":205,"thinkingDisplay":"updates","apiBlockIndex":0,"requestId":"req_011CgXsBDqynn1vxhoXer4pm","requestRef":"f84abb3d-fceb-4117-8147-1be6a23a8d86","requestedModel":"claude-opus-5-5","type":"assistant","uuid":"9b87b968-5129-4993-a795-f737990696de","timestamp":"2026-10-30T05:25:43.932Z","effort":"medium","perTurnEffort":"medium","userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0284","sessionId":"dada2278-6d32-4907-8ce0-67560ca80944","version":"2.1.314","gitBranch":"HEAD"}57{"parentUuid":"9b87b968-5129-4993-a795-f737990696de","isSidechain":false,"message":{"model":"claude-opus-5-5","id":"msg_011CgXsBE6cC3Vj9akmKY8PZ","type":"message","role":"assistant","content":[{"type":"tool_use","id":"toolu_018RTb8LHSsjzTmvg8H5aF7P","name":"Bash","input":{"command":"cat > out/state/checks.md <<'EOF'\n# Verification checks (P4)\n\n| # | check | outcome |\n|---|---|---|\n| 1 | Delete out/results.json, re-run analysis.py, byte-compare with previous output | PASS (cmp identical) |\n| 2 | All gate_spec results_keys present in results.json | PASS (0 missing) |\n| 3 | Every non-boolean content key has a locator | PASS |\n| 4 | Derived DCR CI contains estimate: -0.8426 < -0.431 < -0.0194 | PASS |\n| 5 | Printed CIs contain estimates (wage 166<1153<2141; weeks -2.24<-1.99<-1.75) | PASS |\n| 6 | All SEs positive (0.210, 0.279, 0.187, 0.137, 0.294; derived 503.83) | PASS |\n| 7 | Significance stars in Tables 6/7 agree with normal-approx p from est/SE (18 cells) | PASS (0 mismatches) |\n| 8 | HIV 5 km: no star ⇔ significant_10pct=false; approx p≈0.2475 >0.10 consistent | PASS |\n| 9 | Derived CI excludes 0 consistent with ** (p<0.05) | PASS |\n| 10 | Wage CI near-symmetric (half-widths 987/988) → Wald SE derivation reasonable | PASS |\n| 11 | % change column: exp(-0.431)-1 = -35.0% matches printed | PASS |\n| 12 | n_input_rows equal wc -l (10, 134, 58) | PASS |\n| 13 | Exclusion ledger: 25 − 0 = 25 = n_analysed | PASS |\n| 14 | Input hashes match bundle.json/README | PASS |\n| 15 | Cross-table: Table 6 SE 0.210 vs A19 0.218 | FLAGGED (source inconsistency; reported in data_issues) |\n| 16 | Cross-table: 608 vs 640 control regions; obs/(treated+640)=25 | FLAGGED (reported in data_issues) |\n\nNote: JSON numbers drop trailing zeros (0.210 serialises as 0.21, -0.340 as -0.34); numeric value identical to printed; printed strings are quoted in report.\nEOF\ncat > out/report.md <<'EOF'\n# F4-conf-01 — Treatment effects and uncertainty extracted from two published tables\n\n## Question\nExtract specified point estimates, standard errors, CI bounds and counts exactly as printed from (i) Armendariz (2026), arXiv:2609.27732v1 (staggered DiD, Swiss drug consumption rooms, Tables 6–7) and (ii) Li, Pearson-Merkowitz & Yokum (2026), arXiv:2606.14621v1 (RESEA RCT, Table 2); attach locators; derive a 95% CI from an SE and an SE from a 95% CI; identify the requested quantity that is not reported. Nothing is re-estimated.\n\n## Data and exclusions\nInputs: `data/README.md` (10 lines), `data/sources/arxiv-2609.27732v1.txt` (134 lines), `data/sources/arxiv-2606.14621v1.txt` (58 lines) (`n_input_rows`); SHA-256s match the README/bundle (`input_sha256`). All 25 requested items were assessed (`n_analysed` = 25); nothing was excluded (`out/exclusions.csv`). Five input problems were found and recorded in `data_issues`:\n1. The 5 km CS-DR ATT −0.431 has SE (0.210) in Table 6 but (0.218) in Table A19; Table 6 is used, as the key definitions require.\n2. Tables 6/7 say 608 never-treated control regions, while the Table A19 note says 640. The Table 6 observation counts equal exactly 25 × (treated + 640), not 25 × (treated + 608). The count is reported as printed and the discrepancy is left unresolved.\n3. The abstract says fatal hospitalisations \"fall by 35%\". The Table 7 5 km fatal ATT of −0.354 implies about −29.8%. The text also calls the HIV estimates \"close to zero\" although the 5 km ATT is −0.340. Narrative statements were not used.\n4. The RESEA text gives the weeks-on-UI CI as positive magnitudes [1.75, 2.24]. Table 2 prints [−2.24, −1.75], and the Table 2 values are used.\n5. The RESEA text contains the rendering defect \"p ¡ 0.001\". It does not affect any key.\n\n## Method\n`out/analysis.py` parses the pipe-delimited tables, matches row and column labels, and pairs each coefficient row with the parenthesised SE line that follows it. It removes stars, `$` and thousands separators. It prints every matched source line, then writes `out/results.json`. No randomness is involved (`random_seed` = null). Derived quantities use the normal critical value 1.96 (`derivation_notes`). The baseline, an alternative 2.0 rule, is kept as a sensitivity check (`baseline_dcr_att_5km_ci_rule2`, `baseline_ri_wage_ate_se_rule2`).\n\n## Results\n**DCR (arXiv:2609.27732v1)**\n- Table 6, 5 km: ATT −0.431** (`dcr_att_5km`), SE (0.210) (`dcr_att_5km_se`), Observations 17,250 (`dcr_obs_5km`).\n- Table 6, 3 km: ATT −0.283 (`dcr_att_3km`), SE (0.279) (`dcr_att_3km_se`).\n- Table 7: Narcotics 3 km 0.549*** (0.187) (`dcr_narcotics_att_3km`, `dcr_narcotics_att_3km_se`); Drug-related (fatal) 1 km −1.207*** (0.137) (`dcr_fatal_att_1km`, `dcr_fatal_att_1km_se`); HIV 5 km −0.340 (0.294) with no star (`dcr_hiv_att_5km`, `dcr_hiv_att_5km_se`).\n- The HIV 5 km ATT is not marked significant at 10% (`dcr_hiv_att_5km_significant_10pct` = false). Its exact p-value is **not reported** in the extract (`dcr_hiv_att_5km_p_value` = null).\n- Derived 95% CI of the 5 km ATT: [−0.8426, −0.0194] (`dcr_att_5km_ci_low`, `dcr_att_5km_ci_high`).\n\n**RESEA (arXiv:2606.14621v1), Table 2**\n- Wage ATE $1,153 (`ri_wage_ate`), 95% CI [166, 2,141] (`ri_wage_ate_ci_low`, `ri_wage_ate_ci_high`). No SE is printed (`ri_wage_ate_se_reported` = false). The SE derived from the CI is 503.83 USD/yr (`ri_wage_ate_se_derived`).\n- Control mean wage $33,043 (`ri_control_mean_wage`).\n- Reemployment ATE 1.47 pp (`ri_reemployment_ate_pp`).\n- Weeks-on-UI ATE −1.99 weeks, 95% CI [−2.24, −1.75] (`ri_weeks_ui_ate`, `ri_weeks_ui_ate_ci_low`, `ri_weeks_ui_ate_ci_high`).\n\nJSON numbers do not keep trailing zeros: 0.210 is stored as 0.21 and −0.340 as −0.34. The numeric values are identical to the printed ones.\n\n## Uncertainty\n- **Standard errors:** the DCR paper (Tables 6, 7, A19) reports SEs clustered at the MedStat region level, in parentheses, plus stars (p<0.01/0.05/0.10). It prints no exact p-values or CIs for the table entries.\n- **Confidence intervals:** the RESEA paper (Tables 2, 3) reports 95% CIs and two-sided p-values. It prints no SEs.\n- **The derived DCR CI** (ATT ± 1.96 × SE) assumes the estimator is approximately normally distributed, with the printed clustered SE as its standard deviation. With 50 treated regions a t or bootstrap interval could be wider. A 2.0 rule gives [−0.851, −0.011] (`baseline_dcr_att_5km_ci_rule2`). Using the Table A19 SE of 0.218 instead would also widen the interval.\n- **The derived RESEA SE** ((upper − lower)/3.92) assumes the printed CI is a symmetric Wald interval. This is supported by the near-equal half-widths of 987 and 988. The bounds are rounded to whole dollars, so the derived SE is accurate to about ±0.2. A 2.0 rule gives 493.75 (`baseline_ri_wage_ate_se_rule2`).\n- For context only, a normal approximation of the HIV p-value gives about 0.25 (`check_dcr_hiv_att_5km_normal_approx_p_not_printed`). This is **not** a printed value and is not used for `dcr_hiv_att_5km_p_value`.\n\n## Limitations and open issues\n- The exact p-value of the HIV 5 km ATT is not printed, so it is null by design. The task is otherwise complete (`feasibility` = \"complete\").\n- Two inconsistencies inside the DCR source are unresolved and are reported, not corrected: SE 0.210 vs 0.218, and 608 vs 640 control regions.\n- The derived quantities depend on the normal-approximation convention stated above.\nEOF\ncat > out/claims.json <<'EOF'\n[\n {\"id\":\"C1\",\"text\":\"Table 6 of arXiv:2609.27732v1 prints the 5 km ATT as -0.431** with SE (0.210), the 3 km ATT as -0.283 with 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\":\"Table A19 prints SE 0.218 for the same estimate; Table 6 used as specified.\"},\n {\"id\":\"C2\",\"text\":\"Table 7 prints Narcotics 3 km 0.549*** (0.187), fatal 1 km -1.207*** (0.137) and HIV 5 km -0.340 (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.\"},\n {\"id\":\"C3\",\"text\":\"The exact p-value of the HIV 5 km ATT is not reported, and the estimate carries no significance star (not significant at 10%).\",\"results_keys\":[\"dcr_hiv_att_5km_p_value\",\"dcr_hiv_att_5km_significant_10pct\"],\"status\":\"supported\",\"caveat\":\"A normal-approximation p of about 0.25 is shown only as a non-printed check.\"},\n {\"id\":\"C4\",\"text\":\"The derived 95% CI of the 5 km ATT (1.96 x SE) is [-0.8426, -0.0194].\",\"results_keys\":[\"dcr_att_5km_ci_low\",\"dcr_att_5km_ci_high\",\"derivation_notes\"],\"status\":\"supported\",\"caveat\":\"Assumes normality; 2.0 rule gives [-0.851, -0.011].\"},\n {\"id\":\"C5\",\"text\":\"RESEA Table 2 prints a wage ATE of $1,153 with 95% CI [166, 2,141] and a control mean of $33,043, but no SE; the implied SE is 503.83.\",\"results_keys\":[\"ri_wage_ate\",\"ri_wage_ate_ci_low\",\"ri_wage_ate_ci_high\",\"ri_wage_ate_se_reported\",\"ri_wage_ate_se_derived\",\"ri_control_mean_wage\"],\"status\":\"supported\",\"caveat\":\"Derived SE assumes a symmetric Wald CI with critical value 1.96.\"},\n {\"id\":\"C6\",\"text\":\"RESEA Table 2 prints a reemployment ATE of 1.47 pp and a weeks-on-UI ATE of -1.99 weeks with 95% CI [-2.24, -1.75].\",\"results_keys\":[\"ri_reemployment_ate_pp\",\"ri_weeks_ui_ate\",\"ri_weeks_ui_ate_ci_low\",\"ri_weeks_ui_ate_ci_high\"],\"status\":\"supported\",\"caveat\":\"Text states the weeks CI as positive magnitudes; Table 2 signs used.\"},\n {\"id\":\"C7\",\"text\":\"The DCR source is internally inconsistent on the 5 km SE (0.210 vs 0.218) and on the number of control regions (608 vs 640).\",\"results_keys\":[\"data_issues\"],\"status\":\"supported\",\"caveat\":\"Flagged, not resolved.\"}\n]\nEOF\ncat > out/state/phases.json <<'EOF'\n{\"P0\":{\"status\":\"passed\",\"checks\":[\"TASK.md read fully\",\"contract.md written\"],\"notes\":\"\"},\n \"P1\":{\"status\":\"passed\",\"checks\":[\"SHA-256 of all 3 inputs computed and matched to bundle.json\",\"line counts recorded\",\"schema/anomalies documented in data_audit.md\",\"exclusions.csv: 25-0=25\"],\"notes\":\"5 data issues\"},\n \"P2\":{\"status\":\"passed\",\"checks\":[\"baseline direct lookup and 2.0-rule derivations recorded under baseline_* keys\"],\"notes\":\"\"},\n \"P3\":{\"status\":\"passed\",\"checks\":[\"analysis.py reads only data/, prints matched source lines, writes results.json; no randomness\"],\"notes\":\"\"},\n \"P4\":{\"status\":\"passed\",\"checks\":[\"clean re-run byte-identical (cmp)\",\"all gate keys present\",\"CIs contain estimates\",\"SEs positive\",\"stars consistent with est/SE (0 mismatches of 18)\",\"see checks.md\"],\"notes\":\"2 source inconsistencies flagged\"},\n \"P5\":{\"status\":\"passed\",\"checks\":[\"report.md has 6 required sections with key citations\",\"claims.json keys verified to exist in results.json\"],\"notes\":\"\"}}\nEOF\n~/wp29/venv/bin/python -c \"\nimport json;r=json.load(open('out/results.json'));c=json.load(open('out/claims.json'))\nprint([k for x in c for k in x['results_keys'] if k not in r])\"\necho \"- 2026-10-30 P4 passed (rerun identical, checks.md); P5 passed (report, claims). Task complete.\" >> out/state/log.md","description":"Write checks, report, claims, final phases"},"caller":{"type":"direct"}}],"container":null,"stop_reason":"tool_use","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":2301,"cache_read_input_tokens":35868,"output_tokens":5197,"output_tokens_details":{"thinking_tokens":52},"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":2301},"inference_geo":"global","iterations":[],"speed":"standard","fallback_credit":null},"input_transformations":[],"diagnostics":null,"context_management":null},"apiBlockIndex":1,"requestId":"req_011CgXsBDqynn1vxhoXer4pm","requestRef":"f84abb3d-fceb-4117-8147-1be6a23a8d86","requestedModel":"claude-opus-5-5","type":"assistant","uuid":"5cae83e2-bf42-4836-bc39-a929b6b2638a","timestamp":"2026-10-30T05:26:22.356Z","effort":"medium","perTurnEffort":"medium","userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0284","sessionId":"dada2278-6d32-4907-8ce0-67560ca80944","version":"2.1.314","gitBranch":"HEAD"}58{"parentUuid":"5cae83e2-bf42-4836-bc39-a929b6b2638a","isSidechain":false,"promptId":"52960967-79c0-4a18-8c3d-73f4cb1f259b","type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_018RTb8LHSsjzTmvg8H5aF7P","type":"tool_result","content":"[]","is_error":false}]},"uuid":"1992f695-45af-48ae-b50a-52f22447b37d","timestamp":"2026-10-30T05:26:22.406Z","toolUseResult":{"stdout":"[]","stderr":"","interrupted":false,"isImage":false,"noOutputExpected":false},"sandboxed":false,"sourceToolAssistantUUID":"5cae83e2-bf42-4836-bc39-a929b6b2638a","permissionDecision":{"decision":"accept","source":"config","reasonType":"rule","ruleSource":"cliArg"},"userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0284","sessionId":"dada2278-6d32-4907-8ce0-67560ca80944","version":"2.1.314","gitBranch":"HEAD"}59{"type":"api-request-blob","sessionId":"dada2278-6d32-4907-8ce0-67560ca80944","hash":"9ecfa8e59ff4d1baf74be87ec1881226370d1cdbb15788c8b060d2c4646a6f3f","message":{"role":"assistant","content":[{"type":"thinking","thinking":"","signature":"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"},{"type":"tool_use","id":"toolu_018RTb8LHSsjzTmvg8H5aF7P","name":"Bash","input":{"command":"cat > out/state/checks.md <<'EOF'\n# Verification checks (P4)\n\n| # | check | outcome |\n|---|---|---|\n| 1 | Delete out/results.json, re-run analysis.py, byte-compare with previous output | PASS (cmp identical) |\n| 2 | All gate_spec results_keys present in results.json | PASS (0 missing) |\n| 3 | Every non-boolean content key has a locator | PASS |\n| 4 | Derived DCR CI contains estimate: -0.8426 < -0.431 < -0.0194 | PASS |\n| 5 | Printed CIs contain estimates (wage 166<1153<2141; weeks -2.24<-1.99<-1.75) | PASS |\n| 6 | All SEs positive (0.210, 0.279, 0.187, 0.137, 0.294; derived 503.83) | PASS |\n| 7 | Significance stars in Tables 6/7 agree with normal-approx p from est/SE (18 cells) | PASS (0 mismatches) |\n| 8 | HIV 5 km: no star ⇔ significant_10pct=false; approx p≈0.2475 >0.10 consistent | PASS |\n| 9 | Derived CI excludes 0 consistent with ** (p<0.05) | PASS |\n| 10 | Wage CI near-symmetric (half-widths 987/988) → Wald SE derivation reasonable | PASS |\n| 11 | % change column: exp(-0.431)-1 = -35.0% matches printed | PASS |\n| 12 | n_input_rows equal wc -l (10, 134, 58) | PASS |\n| 13 | Exclusion ledger: 25 − 0 = 25 = n_analysed | PASS |\n| 14 | Input hashes match bundle.json/README | PASS |\n| 15 | Cross-table: Table 6 SE 0.210 vs A19 0.218 | FLAGGED (source inconsistency; reported in data_issues) |\n| 16 | Cross-table: 608 vs 640 control regions; obs/(treated+640)=25 | FLAGGED (reported in data_issues) |\n\nNote: JSON numbers drop trailing zeros (0.210 serialises as 0.21, -0.340 as -0.34); numeric value identical to printed; printed strings are quoted in report.\nEOF\ncat > out/report.md <<'EOF'\n# F4-conf-01 — Treatment effects and uncertainty extracted from two published tables\n\n## Question\nExtract specified point estimates, standard errors, CI bounds and counts exactly as printed from (i) Armendariz (2026), arXiv:2609.27732v1 (staggered DiD, Swiss drug consumption rooms, Tables 6–7) and (ii) Li, Pearson-Merkowitz & Yokum (2026), arXiv:2606.14621v1 (RESEA RCT, Table 2); attach locators; derive a 95% CI from an SE and an SE from a 95% CI; identify the requested quantity that is not reported. Nothing is re-estimated.\n\n## Data and exclusions\nInputs: `data/README.md` (10 lines), `data/sources/arxiv-2609.27732v1.txt` (134 lines), `data/sources/arxiv-2606.14621v1.txt` (58 lines) (`n_input_rows`); SHA-256s match the README/bundle (`input_sha256`). All 25 requested items were assessed (`n_analysed` = 25); nothing was excluded (`out/exclusions.csv`). Five input problems were found and recorded in `data_issues`:\n1. The 5 km CS-DR ATT −0.431 has SE (0.210) in Table 6 but (0.218) in Table A19; Table 6 is used, as the key definitions require.\n2. Tables 6/7 say 608 never-treated control regions, while the Table A19 note says 640. The Table 6 observation counts equal exactly 25 × (treated + 640), not 25 × (treated + 608). The count is reported as printed and the discrepancy is left unresolved.\n3. The abstract says fatal hospitalisations \"fall by 35%\". The Table 7 5 km fatal ATT of −0.354 implies about −29.8%. The text also calls the HIV estimates \"close to zero\" although the 5 km ATT is −0.340. Narrative statements were not used.\n4. The RESEA text gives the weeks-on-UI CI as positive magnitudes [1.75, 2.24]. Table 2 prints [−2.24, −1.75], and the Table 2 values are used.\n5. The RESEA text contains the rendering defect \"p ¡ 0.001\". It does not affect any key.\n\n## Method\n`out/analysis.py` parses the pipe-delimited tables, matches row and column labels, and pairs each coefficient row with the parenthesised SE line that follows it. It removes stars, `$` and thousands separators. It prints every matched source line, then writes `out/results.json`. No randomness is involved (`random_seed` = null). Derived quantities use the normal critical value 1.96 (`derivation_notes`). The baseline, an alternative 2.0 rule, is kept as a sensitivity check (`baseline_dcr_att_5km_ci_rule2`, `baseline_ri_wage_ate_se_rule2`).\n\n## Results\n**DCR (arXiv:2609.27732v1)**\n- Table 6, 5 km: ATT −0.431** (`dcr_att_5km`), SE (0.210) (`dcr_att_5km_se`), Observations 17,250 (`dcr_obs_5km`).\n- Table 6, 3 km: ATT −0.283 (`dcr_att_3km`), SE (0.279) (`dcr_att_3km_se`).\n- Table 7: Narcotics 3 km 0.549*** (0.187) (`dcr_narcotics_att_3km`, `dcr_narcotics_att_3km_se`); Drug-related (fatal) 1 km −1.207*** (0.137) (`dcr_fatal_att_1km`, `dcr_fatal_att_1km_se`); HIV 5 km −0.340 (0.294) with no star (`dcr_hiv_att_5km`, `dcr_hiv_att_5km_se`).\n- The HIV 5 km ATT is not marked significant at 10% (`dcr_hiv_att_5km_significant_10pct` = false). Its exact p-value is **not reported** in the extract (`dcr_hiv_att_5km_p_value` = null).\n- Derived 95% CI of the 5 km ATT: [−0.8426, −0.0194] (`dcr_att_5km_ci_low`, `dcr_att_5km_ci_high`).\n\n**RESEA (arXiv:2606.14621v1), Table 2**\n- Wage ATE $1,153 (`ri_wage_ate`), 95% CI [166, 2,141] (`ri_wage_ate_ci_low`, `ri_wage_ate_ci_high`). No SE is printed (`ri_wage_ate_se_reported` = false). The SE derived from the CI is 503.83 USD/yr (`ri_wage_ate_se_derived`).\n- Control mean wage $33,043 (`ri_control_mean_wage`).\n- Reemployment ATE 1.47 pp (`ri_reemployment_ate_pp`).\n- Weeks-on-UI ATE −1.99 weeks, 95% CI [−2.24, −1.75] (`ri_weeks_ui_ate`, `ri_weeks_ui_ate_ci_low`, `ri_weeks_ui_ate_ci_high`).\n\nJSON numbers do not keep trailing zeros: 0.210 is stored as 0.21 and −0.340 as −0.34. The numeric values are identical to the printed ones.\n\n## Uncertainty\n- **Standard errors:** the DCR paper (Tables 6, 7, A19) reports SEs clustered at the MedStat region level, in parentheses, plus stars (p<0.01/0.05/0.10). It prints no exact p-values or CIs for the table entries.\n- **Confidence intervals:** the RESEA paper (Tables 2, 3) reports 95% CIs and two-sided p-values. It prints no SEs.\n- **The derived DCR CI** (ATT ± 1.96 × SE) assumes the estimator is approximately normally distributed, with the printed clustered SE as its standard deviation. With 50 treated regions a t or bootstrap interval could be wider. A 2.0 rule gives [−0.851, −0.011] (`baseline_dcr_att_5km_ci_rule2`). Using the Table A19 SE of 0.218 instead would also widen the interval.\n- **The derived RESEA SE** ((upper − lower)/3.92) assumes the printed CI is a symmetric Wald interval. This is supported by the near-equal half-widths of 987 and 988. The bounds are rounded to whole dollars, so the derived SE is accurate to about ±0.2. A 2.0 rule gives 493.75 (`baseline_ri_wage_ate_se_rule2`).\n- For context only, a normal approximation of the HIV p-value gives about 0.25 (`check_dcr_hiv_att_5km_normal_approx_p_not_printed`). This is **not** a printed value and is not used for `dcr_hiv_att_5km_p_value`.\n\n## Limitations and open issues\n- The exact p-value of the HIV 5 km ATT is not printed, so it is null by design. The task is otherwise complete (`feasibility` = \"complete\").\n- Two inconsistencies inside the DCR source are unresolved and are reported, not corrected: SE 0.210 vs 0.218, and 608 vs 640 control regions.\n- The derived quantities depend on the normal-approximation convention stated above.\nEOF\ncat > out/claims.json <<'EOF'\n[\n {\"id\":\"C1\",\"text\":\"Table 6 of arXiv:2609.27732v1 prints the 5 km ATT as -0.431** with SE (0.210), the 3 km ATT as -0.283 with 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\":\"Table A19 prints SE 0.218 for the same estimate; Table 6 used as specified.\"},\n {\"id\":\"C2\",\"text\":\"Table 7 prints Narcotics 3 km 0.549*** (0.187), fatal 1 km -1.207*** (0.137) and HIV 5 km -0.340 (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.\"},\n {\"id\":\"C3\",\"text\":\"The exact p-value of the HIV 5 km ATT is not reported, and the estimate carries no significance star (not significant at 10%).\",\"results_keys\":[\"dcr_hiv_att_5km_p_value\",\"dcr_hiv_att_5km_significant_10pct\"],\"status\":\"supported\",\"caveat\":\"A normal-approximation p of about 0.25 is shown only as a non-printed check.\"},\n {\"id\":\"C4\",\"text\":\"The derived 95% CI of the 5 km ATT (1.96 x SE) is [-0.8426, -0.0194].\",\"results_keys\":[\"dcr_att_5km_ci_low\",\"dcr_att_5km_ci_high\",\"derivation_notes\"],\"status\":\"supported\",\"caveat\":\"Assumes normality; 2.0 rule gives [-0.851, -0.011].\"},\n {\"id\":\"C5\",\"text\":\"RESEA Table 2 prints a wage ATE of $1,153 with 95% CI [166, 2,141] and a control mean of $33,043, but no SE; the implied SE is 503.83.\",\"results_keys\":[\"ri_wage_ate\",\"ri_wage_ate_ci_low\",\"ri_wage_ate_ci_high\",\"ri_wage_ate_se_reported\",\"ri_wage_ate_se_derived\",\"ri_control_mean_wage\"],\"status\":\"supported\",\"caveat\":\"Derived SE assumes a symmetric Wald CI with critical value 1.96.\"},\n {\"id\":\"C6\",\"text\":\"RESEA Table 2 prints a reemployment ATE of 1.47 pp and a weeks-on-UI ATE of -1.99 weeks with 95% CI [-2.24, -1.75].\",\"results_keys\":[\"ri_reemployment_ate_pp\",\"ri_weeks_ui_ate\",\"ri_weeks_ui_ate_ci_low\",\"ri_weeks_ui_ate_ci_high\"],\"status\":\"supported\",\"caveat\":\"Text states the weeks CI as positive magnitudes; Table 2 signs used.\"},\n {\"id\":\"C7\",\"text\":\"The DCR source is internally inconsistent on the 5 km SE (0.210 vs 0.218) and on the number of control regions (608 vs 640).\",\"results_keys\":[\"data_issues\"],\"status\":\"supported\",\"caveat\":\"Flagged, not resolved.\"}\n]\nEOF\ncat > out/state/phases.json <<'EOF'\n{\"P0\":{\"status\":\"passed\",\"checks\":[\"TASK.md read fully\",\"contract.md written\"],\"notes\":\"\"},\n \"P1\":{\"status\":\"passed\",\"checks\":[\"SHA-256 of all 3 inputs computed and matched to bundle.json\",\"line counts recorded\",\"schema/anomalies documented in data_audit.md\",\"exclusions.csv: 25-0=25\"],\"notes\":\"5 data issues\"},\n \"P2\":{\"status\":\"passed\",\"checks\":[\"baseline direct lookup and 2.0-rule derivations recorded under baseline_* keys\"],\"notes\":\"\"},\n \"P3\":{\"status\":\"passed\",\"checks\":[\"analysis.py reads only data/, prints matched source lines, writes results.json; no randomness\"],\"notes\":\"\"},\n \"P4\":{\"status\":\"passed\",\"checks\":[\"clean re-run byte-identical (cmp)\",\"all gate keys present\",\"CIs contain estimates\",\"SEs positive\",\"stars consistent with est/SE (0 mismatches of 18)\",\"see checks.md\"],\"notes\":\"2 source inconsistencies flagged\"},\n \"P5\":{\"status\":\"passed\",\"checks\":[\"report.md has 6 required sections with key citations\",\"claims.json keys verified to exist in results.json\"],\"notes\":\"\"}}\nEOF\n~/wp29/venv/bin/python -c \"\nimport json;r=json.load(open('out/results.json'));c=json.load(open('out/claims.json'))\nprint([k for x in c for k in x['results_keys'] if k not in r])\"\necho \"- 2026-10-30 P4 passed (rerun identical, checks.md); P5 passed (report, claims). Task complete.\" >> out/state/log.md","description":"Write checks, report, claims, final phases"}}]}}60{"type":"api-request","sessionId":"dada2278-6d32-4907-8ce0-67560ca80944","id":"caba292a-5e08-4170-8cec-06b5ff3e618c","timestamp":"2026-10-30T05:26:22.408Z","version":"2.1.314","querySource":"sdk","shapeHash":"6e2648af828e8ce4652445d86f372b634cebc8534a8052871075924b6682d1b1","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":"f84abb3d-fceb-4117-8147-1be6a23a8d86","keep":16,"tail":["9ecfa8e59ff4d1baf74be87ec1881226370d1cdbb15788c8b060d2c4646a6f3f","e30d25276782fc30083474bab7c18ef7d8531de3521fa7e6b79bc84a0f778700"],"cacheControl":[{"path":["system",0],"value":{"type":"ephemeral"}},{"path":["system",1],"value":{"type":"ephemeral"}},{"path":["messages",17,"content",0],"value":{"type":"ephemeral"}}],"requestDigest":"a934fed1232979a7f52153c349f3dc17dcffe7b1ff9a95219e049bf2f6ee061c"}61{"type":"last-prompt","lastPrompt":"Begin","leafUuid":"1992f695-45af-48ae-b50a-52f22447b37d","sessionId":"dada2278-6d32-4907-8ce0-67560ca80944"}62{"type":"atis-latch","atis":"","sessionId":"dada2278-6d32-4907-8ce0-67560ca80944"}63{"parentUuid":"1992f695-45af-48ae-b50a-52f22447b37d","isSidechain":false,"message":{"model":"claude-opus-5-5","id":"msg_011CgXsECTDdkEavELT7PHgy","type":"message","role":"assistant","content":[{"type":"text","text":"I've extracted all 25 requested values from the two papers, and the task is complete. The one requested quantity the source doesn't print, the exact p-value for the HIV 5 km effect, is reported as `null`. Running `out/analysis.py` again from a clean state produced an identical `out/results.json`.\n\n**Drug consumption room study (Armendariz 2026)**\n- **Table 6, 5 km:** effect −0.431** with standard error (0.210), on 17,250 observations.\n- **Table 6, 3 km:** effect −0.283, standard error (0.279).\n- **Table 7:** narcotics at 3 km 0.549*** (0.187); fatal at 1 km −1.207*** (0.137); HIV at 5 km −0.340 (0.294), with no star.\n- **HIV 5 km:** the paper marks no significance at 10%, and prints no exact p-value anywhere. For context only, a normal approximation gives about 0.25; it is labelled as not printed and is not used as the answer.\n- **Derived 95% CI for the 5 km effect:** [−0.8426, −0.0194], using estimate ± 1.96 × standard error.\n\n**Rhode Island RESEA trial (Li et al. 2026), Table 2**\n- **Wage effect:** $1,153 per year, 95% CI [166, 2,141]. No standard error is printed. The one implied by the CI is 503.83, assuming a symmetric interval with 1.96.\n- **Control-group mean wage:** $33,043.\n- **Reemployment effect:** 1.47 percentage points.\n- **Weeks on unemployment insurance:** −1.99 weeks, 95% CI [−2.24, −1.75].\n\n**Problems in the sources** (flagged, not corrected):\n- **Conflicting standard errors:** the same 5 km effect has standard error 0.210 in Table 6 but 0.218 in Table A19. I used Table 6, as the task specifies.\n- **Conflicting control counts:** Tables 6 and 7 say 608 control regions, but the Table A19 note says 640. The observation counts work out to exactly 25 × (treated + 640), so 640 may be the right figure. I reported 17,250 as printed and left this unresolved.\n- **Text versus tables:** the abstract says fatal hospitalisations fall 35%, but the Table 7 figure implies about 30%. The RESEA text gives the weeks CI as positive numbers, while Table 2 prints them negative. There is also one garbled character (\"p ¡ 0.001\"). None of these affected the extracted values.\n\n**Limitations:**\n- **Derived numbers:** the CI and the implied standard error assume an approximately normal estimate. With a critical value of 2.0 instead, they would be [−0.851, −0.011] and 493.75.\n- **Trailing zeros:** the results file stores 0.210 as 0.21 and −0.340 as −0.34. 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