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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/RT0019","sessionId":"47ce3250-2468-470f-8e8f-38dfc848301c","version":"2.1.314","gitBranch":"HEAD"}11{"type":"tools-used","tools":["Bash"],"sessionId":"47ce3250-2468-470f-8e8f-38dfc848301c"}12{"parentUuid":"2694a69e-2f9e-4df8-b103-302bfd20ac89","isSidechain":false,"message":{"model":"claude-opus-5-5","id":"msg_011CgXsy7Ft3BF2KyEurYmEH","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":126,"output_tokens_details":{"thinking_tokens":20},"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":513,"thinkingDisplay":"updates","apiBlockIndex":0,"requestId":"req_011CgXsy6ykrEURKoqjLHLLJ","requestRef":"77f34fa4-3372-4044-bfe3-77c5c68e1fa5","requestedModel":"claude-opus-5-5","type":"assistant","uuid":"08fd36ea-3571-4e31-bba1-38262e27d89c","timestamp":"2026-10-30T05:36:05.763Z","effort":"medium","perTurnEffort":"medium","userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RT0019","sessionId":"47ce3250-2468-470f-8e8f-38dfc848301c","version":"2.1.314","gitBranch":"HEAD"}13{"parentUuid":"08fd36ea-3571-4e31-bba1-38262e27d89c","isSidechain":false,"message":{"model":"claude-opus-5-5","id":"msg_011CgXsy7Ft3BF2KyEurYmEH","type":"message","role":"assistant","content":[{"type":"tool_use","id":"toolu_01Y9F2nkhUvihBAedxHtfrR5","name":"Bash","input":{"command":"cat TASK.md; 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This is a complete simulation-methodology project: design → implementation → Monte Carlo evaluation with its own uncertainty → identification of failure regimes → figure and table → an executable technical note. The estimand of the inferential procedures is the population mean μ of y, estimated by the sample mean ȳ of N clustered observations; the estimands of the study are, for each of the nine design cells (G ∈ {5, 10, 30} clusters × intra-cluster correlation ICC ∈ {0.0, 0.2, 0.5}) and each of three interval procedures, (a) the actual coverage probability of the nominal 95% interval, (b) for the two sandwich estimators, the relative bias of the variance estimator, E[V̂] / Var(ȳ), where Var(ȳ) is the exact variance of the sample mean under the design, (c) the Monte Carlo standard error of each coverage estimate, and (d) the set of (procedure, cell) pairs whose coverage falls below 0.90 — the failure regimes.\n\n## Inputs\n- `data/design.csv` — simulated-data design written by the task designer (nothing here is observed data): one row per cell with `cell_id`, `G`, `icc`, `cluster_sizes` (the G cluster sizes separated by `|`: the pattern 8, 16, 32, 64 repeated), `N`, `R` = 2000 replications, `B` = 399 bootstrap draws, `seed`, `mu` = 0, `total_variance` = 1, `nominal_level` = 0.95. See `data/README.md`.\n\n## Information-availability rules\n- Data-generating process (per replication): y_ig = μ + u_g + e_ig with u_g ~ N(0, ICC) i.i.d. across clusters and e_ig ~ N(0, 1 − ICC) i.i.d. across observations, independent of u; cluster g has n_g observations as listed in `cluster_sizes`. Exact variance of the sample mean: Var(ȳ) = [ICC · Σ_g n_g² + (1 − ICC) · N] / N².\n- Interval procedures (all three required, exactly as specified, with ȳ as the point estimate and residuals ê_ig = y_ig − ȳ):\n  - **CR0**: V̂_CR0 = Σ_g (Σ_i ê_ig)² / N²; interval ȳ ± 1.96 · √V̂_CR0;\n  - **CR1**: V̂_CR1 = [G / (G − 1)] · V̂_CR0; interval ȳ ± t_{0.975, G−1} · √V̂_CR1 (Student t with G − 1 degrees of freedom);\n  - **WCB** (wild cluster bootstrap-t, unrestricted, Rademacher): for b = 1..B draw cluster weights w_g^b ∈ {−1, +1} with equal probability, form y*_ig = ȳ + w_g^b · ê_ig, compute ȳ* and its V̂_CR1* on the bootstrap sample, t*_b = (ȳ* − ȳ) / √V̂_CR1*; critical value q = the 0.95 empirical quantile of |t*_1|, …, |t*_B|; interval ȳ ± q · √V̂_CR1.\n- Use R = 2000 replications per cell, B = 399 bootstrap draws per replication and the cell's seed from `design.csv`; do not change the grid, R or B. A procedure's coverage in a cell is the fraction of the R intervals that contain μ. The Monte Carlo standard error of a coverage estimate p̂ is √(p̂(1 − p̂)/R).\n- A (procedure, cell) pair is a **failure regime** when its estimated coverage is below 0.90. A pair is **consistent with nominal coverage** when |p̂ − 0.95| ≤ 2 · MC SE.\n\n## Required outputs\n- `out/table1.csv` — 27 rows (3 procedures × 9 cells) with columns `estimator` (`cr0`, `cr1`, `wcb`), `cell_id`, `G`, `icc`, `coverage`, `mc_se_coverage`, `rel_bias_var` (empty for `wcb`), `mean_ci_halfwidth`, `R`.\n- `out/results.json` with the keys:\n\n| key | type | unit | meaning |\n|---|---|---|---|\n| `task_id` | string | — | \"T2-simulation\" |\n| `R` | integer | — | replications per cell actually used |\n| `B` | integer | — | bootstrap draws per replication actually used |\n| `n_cells` | integer | — | number of design cells simulated |\n| `coverage` | object | — | {\"<estimator>_G<G>_icc<icc>\": number} for the 27 pairs, e.g. `\"cr1_G5_icc0.2\"` (icc written as 0.0, 0.2, 0.5) |\n| `mc_se_coverage` | object | — | same keys: Monte Carlo SE of each coverage estimate |\n| `rel_bias_var` | object | — | 18 keys (cr0 and cr1 only): E[V̂]/Var(ȳ) estimated by the Monte Carlo mean |\n| `failure_cells` | array of strings | — | the pairs with coverage < 0.90, same key format, any order |\n| `n_failure_cells` | integer | — | length of `failure_cells` |\n| `nominal_cells` | array of strings | — | the pairs whose coverage is consistent with nominal (|p̂ − 0.95| ≤ 2 MC SE) |\n| `best_procedure_small_G` | string | — | the procedure with the highest average coverage over the three G = 5 cells |\n| `feasibility` | string | — | \"complete\", \"partial\" or \"infeasible\" |\n| `feasibility_reason` | string or null | — | explanation if not complete |\n| `data_issues` | array of {\"issue\",\"handling\"} | — | problems detected in the design or during the simulation and how handled; empty list if none |\n| `n_input_rows` | object | rows | {\"design.csv\": number of data rows} |\n| `n_analysed` | integer or null | — | total number of replications simulated (cells × R) |\n| `random_seed` | integer or null | — | the seed of cell C1 (the other seeds are in `design.csv`) |\n\n- `out/figure1.png` — coverage against G for the three procedures, one panel per ICC (or an equivalent figure), with the nominal level and the 0.90 failure line marked.\n- `out/report.md` — an executable technical note with the sections: Question; Data and exclusions; Method; Results; Uncertainty; Limitations and open issues. \"Uncertainty\" must discuss the Monte Carlo error of the coverage and bias estimates; \"Limitations and open issues\" must state what the design does not cover (distributions, heterogeneity, regressors, other weights).\n- `out/analysis.py` — running `~/wp29/venv/bin/python out/analysis.py` from the workspace root must regenerate `out/results.json`, `out/table1.csv` and `out/figure1.png` from `data/design.csv` only, deterministically (same seeds → same numbers).\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, matplotlib available). Do not modify `data/`.\n- Vectorise the bootstrap (a B × G weight matrix per replication is enough); the full grid runs in a few minutes in numpy. The analysis must complete within 10 minutes.\n- Budget class: transfer mini-project — 12.00 USD cap, 60 minutes wall, 150 turns per session. The project is designed to be completed in well under 45 minutes of work.\ntotal 32\ndrwxr-xr-x    6 simon-pierreboucher  staff   192 oct.  30 00:18 .\ndrwxr-xr-x  186 simon-pierreboucher  staff  5952 oct.  30 01:36 ..\n-rw-r--r--    1 simon-pierreboucher  staff   517 oct.  30 00:32 bundle.json\ndrwxr-xr-x    4 simon-pierreboucher  staff   128 oct.  30 00:12 data\n-rw-r--r--    1 simon-pierreboucher  staff  1545 oct.  30 00:12 gate_spec.json\n-rw-r--r--    1 simon-pierreboucher  staff  6628 oct.  30 00:12 TASK.md\ndata:\ndesign.csv\nREADME.md","is_error":false}]},"uuid":"c49ac558-3c25-4aca-bcbe-bf8b27c4b648","timestamp":"2026-10-30T05:36:06.394Z","toolUseResult":{"stdout":"# T2-simulation — Finite-sample performance of three cluster-robust confidence intervals for a mean: a simulation study\n\n## Scientific question\nWhen observations are grouped in a small number of clusters, how reliable are the usual cluster-robust confidence intervals for a population mean? This is a complete simulation-methodology project: design → implementation → Monte Carlo evaluation with its own uncertainty → identification of failure regimes → figure and table → an executable technical note. The estimand of the inferential procedures is the population mean μ of y, estimated by the sample mean ȳ of N clustered observations; the estimands of the study are, for each of the nine design cells (G ∈ {5, 10, 30} clusters × intra-cluster correlation ICC ∈ {0.0, 0.2, 0.5}) and each of three interval procedures, (a) the actual coverage probability of the nominal 95% interval, (b) for the two sandwich estimators, the relative bias of the variance estimator, E[V̂] / Var(ȳ), where Var(ȳ) is the exact variance of the sample mean under the design, (c) the Monte Carlo standard error of each coverage estimate, and (d) the set of (procedure, cell) pairs whose coverage falls below 0.90 — the failure regimes.\n\n## Inputs\n- `data/design.csv` — simulated-data design written by the task designer (nothing here is observed data): one row per cell with `cell_id`, `G`, `icc`, `cluster_sizes` (the G cluster sizes separated by `|`: the pattern 8, 16, 32, 64 repeated), `N`, `R` = 2000 replications, `B` = 399 bootstrap draws, `seed`, `mu` = 0, `total_variance` = 1, `nominal_level` = 0.95. See `data/README.md`.\n\n## Information-availability rules\n- Data-generating process (per replication): y_ig = μ + u_g + e_ig with u_g ~ N(0, ICC) i.i.d. across clusters and e_ig ~ N(0, 1 − ICC) i.i.d. across observations, independent of u; cluster g has n_g observations as listed in `cluster_sizes`. Exact variance of the sample mean: Var(ȳ) = [ICC · Σ_g n_g² + (1 − ICC) · N] / N².\n- Interval procedures (all three required, exactly as specified, with ȳ as the point estimate and residuals ê_ig = y_ig − ȳ):\n  - **CR0**: V̂_CR0 = Σ_g (Σ_i ê_ig)² / N²; interval ȳ ± 1.96 · √V̂_CR0;\n  - **CR1**: V̂_CR1 = [G / (G − 1)] · V̂_CR0; interval ȳ ± t_{0.975, G−1} · √V̂_CR1 (Student t with G − 1 degrees of freedom);\n  - **WCB** (wild cluster bootstrap-t, unrestricted, Rademacher): for b = 1..B draw cluster weights w_g^b ∈ {−1, +1} with equal probability, form y*_ig = ȳ + w_g^b · ê_ig, compute ȳ* and its V̂_CR1* on the bootstrap sample, t*_b = (ȳ* − ȳ) / √V̂_CR1*; critical value q = the 0.95 empirical quantile of |t*_1|, …, |t*_B|; interval ȳ ± q · √V̂_CR1.\n- Use R = 2000 replications per cell, B = 399 bootstrap draws per replication and the cell's seed from `design.csv`; do not change the grid, R or B. A procedure's coverage in a cell is the fraction of the R intervals that contain μ. The Monte Carlo standard error of a coverage estimate p̂ is √(p̂(1 − p̂)/R).\n- A (procedure, cell) pair is a **failure regime** when its estimated coverage is below 0.90. A pair is **consistent with nominal coverage** when |p̂ − 0.95| ≤ 2 · MC SE.\n\n## Required outputs\n- `out/table1.csv` — 27 rows (3 procedures × 9 cells) with columns `estimator` (`cr0`, `cr1`, `wcb`), `cell_id`, `G`, `icc`, `coverage`, `mc_se_coverage`, `rel_bias_var` (empty for `wcb`), `mean_ci_halfwidth`, `R`.\n- `out/results.json` with the keys:\n\n| key | type | unit | meaning |\n|---|---|---|---|\n| `task_id` | string | — | \"T2-simulation\" |\n| `R` | integer | — | replications per cell actually used |\n| `B` | integer | — | bootstrap draws per replication actually used |\n| `n_cells` | integer | — | number of design cells simulated |\n| `coverage` | object | — | {\"<estimator>_G<G>_icc<icc>\": number} for the 27 pairs, e.g. `\"cr1_G5_icc0.2\"` (icc written as 0.0, 0.2, 0.5) |\n| `mc_se_coverage` | object | — | same keys: Monte Carlo SE of each coverage estimate |\n| `rel_bias_var` | object | — | 18 keys (cr0 and cr1 only): E[V̂]/Var(ȳ) estimated by the Monte Carlo mean |\n| `failure_cells` | array of strings | — | the pairs with coverage < 0.90, same key format, any order |\n| `n_failure_cells` | integer | — | length of `failure_cells` |\n| `nominal_cells` | array of strings | — | the pairs whose coverage is consistent with nominal (|p̂ − 0.95| ≤ 2 MC SE) |\n| `best_procedure_small_G` | string | — | the procedure with the highest average coverage over the three G = 5 cells |\n| `feasibility` | string | — | \"complete\", \"partial\" or \"infeasible\" |\n| `feasibility_reason` | string or null | — | explanation if not complete |\n| `data_issues` | array of {\"issue\",\"handling\"} | — | problems detected in the design or during the simulation and how handled; empty list if none |\n| `n_input_rows` | object | rows | {\"design.csv\": number of data rows} |\n| `n_analysed` | integer or null | — | total number of replications simulated (cells × R) |\n| `random_seed` | integer or null | — | the seed of cell C1 (the other seeds are in `design.csv`) |\n\n- `out/figure1.png` — coverage against G for the three procedures, one panel per ICC (or an equivalent figure), with the nominal level and the 0.90 failure line marked.\n- `out/report.md` — an executable technical note with the sections: Question; Data and exclusions; Method; Results; Uncertainty; Limitations and open issues. \"Uncertainty\" must discuss the Monte Carlo error of the coverage and bias estimates; \"Limitations and open issues\" must state what the design does not cover (distributions, heterogeneity, regressors, other weights).\n- `out/analysis.py` — running `~/wp29/venv/bin/python out/analysis.py` from the workspace root must regenerate `out/results.json`, `out/table1.csv` and `out/figure1.png` from `data/design.csv` only, deterministically (same seeds → same numbers).\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, matplotlib available). Do not modify `data/`.\n- Vectorise the bootstrap (a B × G weight matrix per replication is enough); the full grid runs in a few minutes in numpy. The analysis must complete within 10 minutes.\n- Budget class: transfer mini-project — 12.00 USD cap, 60 minutes wall, 150 turns per session. 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The only input is `design.csv`, written by the task designer: the nine cells of the Monte Carlo design (one row per cell).\n\n| column | meaning |\n|---|---|\n| `cell_id` | C1 … C9 |\n| `G` | number of clusters (5, 10, 30) |\n| `icc` | intra-cluster correlation of the data-generating process (0.0, 0.2, 0.5) |\n| `cluster_sizes` | the G cluster sizes n_1, …, n_G, separated by `|` (the pattern 8, 16, 32, 64 repeated) |\n| `N` | total number of observations in a sample |\n| `R` | number of Monte Carlo replications per cell (2000) |\n| `B` | number of wild-bootstrap draws per replication (399) |\n| `seed` | the seed to use for the cell (numpy `default_rng(seed)` or equivalent) |\n| `mu` | population mean of y (0.0) |\n| `total_variance` | Var(y_ig) = Var(u_g) + Var(e_ig) = 1.0 |\n| `nominal_level` | nominal confidence level (0.95) |\n\nData-generating process for a cell: y_ig = mu + u_g + e_ig, g = 1..G, i = 1..n_g, with u_g ~ N(0, icc) independent across clusters and e_ig ~ N(0, 1 − icc) independent across observations and of u. All simulated data are synthetic; nothing here is observed data.\ncell_id,G,icc,cluster_sizes,N,R,B,seed,mu,total_variance,nominal_level\r\nC1,5,0.0,8|16|32|64|8,128,2000,399,20261101,0.0,1.0,0.95\r\nC2,5,0.2,8|16|32|64|8,128,2000,399,20261102,0.0,1.0,0.95\r\nC3,5,0.5,8|16|32|64|8,128,2000,399,20261103,0.0,1.0,0.95\r\nC4,10,0.0,8|16|32|64|8|16|32|64|8|16,264,2000,399,20261104,0.0,1.0,0.95\r\nC5,10,0.2,8|16|32|64|8|16|32|64|8|16,264,2000,399,20261105,0.0,1.0,0.95\r\nC6,10,0.5,8|16|32|64|8|16|32|64|8|16,264,2000,399,20261106,0.0,1.0,0.95\r\nC7,30,0.0,8|16|32|64|8|16|32|64|8|16|32|64|8|16|32|64|8|16|32|64|8|16|32|64|8|16|32|64|8|16,864,2000,399,20261107,0.0,1.0,0.95\r\nC8,30,0.2,8|16|32|64|8|16|32|64|8|16|32|64|8|16|32|64|8|16|32|64|8|16|32|64|8|16|32|64|8|16,864,2000,399,20261108,0.0,1.0,0.95\r\nC9,30,0.5,8|16|32|64|8|16|32|64|8|16|32|64|8|16|32|64|8|16|32|64|8|16|32|64|8|16|32|64|8|16,864,2000,399,20261109,0.0,1.0,0.95\r\nf4ff004ed50d893eece1aaa7edced04d3c0439aaedf1d391ea4642a87d0ecc50  data/design.csv\n73a6595909d92a69db281e283f193348ac244f255fcd5343ef42ae0353394e67  data/README.md\n{\n  \"task_id\": \"T2-simulation\",\n  \"family\": \"T2\",\n  \"split\": \"transfer\",\n  \"version\": \"1.0\",\n  \"created_utc\": \"2026-10-30T04:32:27.189577+00:00\",\n  \"files\": {\n    \"TASK.md\": \"22849122c96cf5f55a070b010b46d61fcd43177c89a6278a0e8dfc609113cb73\",\n    \"data/README.md\": \"73a6595909d92a69db281e283f193348ac244f255fcd5343ef42ae0353394e67\",\n    \"data/design.csv\": \"f4ff004ed50d893eece1aaa7edced04d3c0439aaedf1d391ea4642a87d0ecc50\",\n    \"gate_spec.json\": \"30f9d1aec40a303349e47e2a432ac95c1403984a0b951eefb117ace32d657e1e\"\n  }\n}{\n  \"task_id\": \"T2-simulation\",\n  \"required_files\": [\"out/results.json\", \"out/report.md\", \"out/analysis.py\", \"out/claims.json\", \"out/table1.csv\", \"out/figure1.png\"],\n  \"results_keys\": {\n    \"task_id\": {\"type\": \"string\", \"nullable\": false},\n    \"R\": {\"type\": \"integer\", \"nullable\": false},\n    \"B\": {\"type\": \"integer\", \"nullable\": false},\n    \"n_cells\": {\"type\": \"integer\", \"nullable\": false},\n    \"coverage\": {\"type\": \"object\", \"nullable\": false},\n    \"mc_se_coverage\": {\"type\": \"object\", \"nullable\": false},\n    \"rel_bias_var\": {\"type\": \"object\", \"nullable\": false},\n    \"failure_cells\": {\"type\": \"array\", \"nullable\": false},\n    \"n_failure_cells\": {\"type\": \"integer\", \"nullable\": false},\n    \"nominal_cells\": {\"type\": \"array\", \"nullable\": false},\n    \"best_procedure_small_G\": {\"type\": \"string\", \"nullable\": false},\n    \"feasibility\": {\"type\": \"string\", \"nullable\": false},\n    \"feasibility_reason\": {\"type\": \"string\", \"nullable\": true},\n    \"data_issues\": {\"type\": \"array\", \"nullable\": false},\n    \"n_input_rows\": {\"type\": \"object\", \"nullable\": false},\n    \"n_analysed\": {\"type\": \"integer\", \"nullable\": true},\n    \"random_seed\": {\"type\": \"integer\", \"nullable\": true}\n  },\n  \"report_sections\": [\"Question\", \"Data and exclusions\", \"Method\", \"Results\", \"Uncertainty\", \"Limitations and open issues\"],\n  \"family_checks\": [\"claims_reference_existing_keys\", \"phase_ledger_consistent\", \"exclusion_ledger_sums\"],\n  \"ci_pairs\": [],\n  \"positive_keys\": [],\n  \"chronology_pairs\": [],\n  \"analysis_timeout_s\": 600,\n  \"reproducibility_tolerance_rel\": 1e-6\n}","is_error":false}]},"uuid":"505e4b14-5525-4f95-bc32-d74b07302071","timestamp":"2026-10-30T05:36:08.165Z","toolUseResult":{"stdout":"# Data — T2-simulation\n\nThis project uses **no observed data**. The only input is `design.csv`, written by the task designer: the nine cells of the Monte Carlo design (one row per cell).\n\n| column | meaning |\n|---|---|\n| `cell_id` | C1 … C9 |\n| `G` | number of clusters (5, 10, 30) |\n| `icc` | intra-cluster correlation of the data-generating process (0.0, 0.2, 0.5) |\n| `cluster_sizes` | the G cluster sizes n_1, …, n_G, separated by `|` (the pattern 8, 16, 32, 64 repeated) |\n| `N` | total number of observations in a sample |\n| `R` | number of Monte Carlo replications per cell (2000) |\n| `B` | number of wild-bootstrap draws per replication (399) |\n| `seed` | the seed to use for the cell (numpy `default_rng(seed)` or equivalent) |\n| `mu` | population mean of y (0.0) |\n| `total_variance` | Var(y_ig) = Var(u_g) + Var(e_ig) = 1.0 |\n| `nominal_level` | nominal confidence level (0.95) |\n\nData-generating process for a cell: y_ig = mu + u_g + e_ig, g = 1..G, i = 1..n_g, with u_g ~ N(0, icc) independent across clusters and e_ig ~ N(0, 1 − icc) independent across observations and of u. 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-p out/state && cd out/state && cat > contract.md <<'EOF'\n# Contract (P0)\n**Question.** With few clusters, how reliable are cluster-robust 95% CIs for a population mean μ estimated by the sample mean ȳ?\n**Estimands of the study**, for each of 9 cells (G∈{5,10,30} × ICC∈{0,0.2,0.5}) and procedures CR0, CR1, WCB:\n(a) actual coverage of the nominal 95% interval (fraction of R=2000 intervals containing μ=0);\n(b) for CR0/CR1, relative bias E[V̂]/Var(ȳ), with exact Var(ȳ)=[ICC Σn_g² + (1−ICC)N]/N², E[V̂] estimated by MC mean;\n(c) MC SE of coverage √(p̂(1−p̂)/R);\n(d) failure regimes: pairs with coverage < 0.90; also \"nominal\" pairs |p̂−0.95| ≤ 2 MC SE.\nAssumptions: DGP y=μ+u_g+e_ig, Gaussian, as specified; procedures exactly as in TASK.md (CR0 z=1.96; CR1 t_{G−1}; WCB unrestricted Rademacher bootstrap-t with B=399, critical value = 0.95 empirical quantile of |t*|).\n**Outputs:** out/table1.csv (27 rows: estimator,cell_id,G,icc,coverage,mc_se_coverage,rel_bias_var[empty for 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/Users/simon-pierreboucher/wp29/runs/RT0019 && ~/wp29/venv/bin/python - <<'EOF'\nimport pandas as pd\nd=pd.read_csv('data/design.csv')\nprint(d.dtypes); print(d.isna().sum().sum(), d.duplicated().sum(), d.cell_id.duplicated().sum())\nfor _,r in d.iterrows():\n    n=[int(x) for x in r.cluster_sizes.split('|')]\n    print(r.cell_id,len(n)==r.G,sum(n)==r.N, n==([8,16,32,64]*10)[:r.G])\nEOF","description":"Audit design.csv 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print(d.isna().sum().sum(), d.duplicated().sum(), d.cell_id.duplicated().sum())\nfor _,r in d.iterrows():\n    n=[int(x) for x in r.cluster_sizes.split('|')]\n    print(r.cell_id,len(n)==r.G,sum(n)==r.N, n==([8,16,32,64]*10)[:r.G])\nEOF","description":"Audit design.csv consistency"}}]}}37{"type":"api-request","sessionId":"47ce3250-2468-470f-8e8f-38dfc848301c","id":"497c2e8b-ac20-44ea-931a-d551168d51d1","timestamp":"2026-10-30T05:36:30.785Z","version":"2.1.314","querySource":"sdk","shapeHash":"73d3b570cb757bc7e5d014e30ddf95327c833e905d81c3e64645bb812b4dbf54","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":"d0f4ab2e-4487-4e71-983b-fbf30b2cb40c","keep":10,"tail":["eaf74ba9eded45ec98d62cebafa7a1c73e2c6763bdb93084a6e58a43cd250107","fae7ab3b97608ddffce721c8346142e7835a94d0c987a21a7300943d82888400"],"cacheControl":[{"path":["system",0],"value":{"type":"ephemeral"}},{"path":["system",1],"value":{"type":"ephemeral"}},{"path":["messages",11,"content",0],"value":{"type":"ephemeral"}}],"requestDigest":"65377a261117e4ff0c7b7649c925353ac756abb5d99da2dba09198fb606d570d"}38{"parentUuid":"559c2596-b1d9-4e97-acea-33176c68b087","isSidechain":false,"message":{"model":"claude-opus-5-5","id":"msg_011CgXt13t2WD7w7jkfAjonv","type":"message","role":"assistant","content":[{"type":"tool_use","id":"toolu_015gocsdUHnuUHtsQvqDqCHS","name":"Bash","input":{"command":"cd /Users/simon-pierreboucher/wp29/runs/RT0019/out && cat > state/data_audit.md <<'EOF'\n# Data audit (P1)\n| file | SHA-256 | rows | cols |\n|---|---|---|---|\n| data/design.csv | f4ff004ed50d893eece1aaa7edced04d3c0439aaedf1d391ea4642a87d0ecc50 | 9 | 11 |\n| data/README.md | 73a6595909d92a69db281e283f193348ac244f255fcd5343ef42ae0353394e67 | (text) | — |\nHashes match bundle.json.\n\nSchema: cell_id(str C1..C9), G(int 5/10/30), icc(float 0/0.2/0.5), cluster_sizes(str '|'-sep ints), N(int), R=2000, B=399, seed(int 20261101..20261109), mu=0.0, total_variance=1.0, nominal_level=0.95. No dates (synthetic design).\nMissing values: 0. Duplicate rows: 0. Duplicate cell_id: 0.\nConsistency: for all 9 rows len(cluster_sizes)=G, sum(cluster_sizes)=N (128, 264, 864), and sizes follow 8,16,32,64 repeated. \n\nAnomalies / notes (not errors):\n- ICC=0.0 cells: u_g ~ N(0,0) is degenerate (u_g ≡ 0); handled by drawing u_g = sqrt(icc)*Z, which is exactly 0.\n- G=5 with Rademacher weights: only 2^5=32 distinct weight vectors, and |t*| is invariant to flipping all signs, so at most 16 distinct |t*| values; the B=399 bootstrap quantile is therefore highly discrete (known WCB limitation with very few clusters). Kept as specified; reported in data_issues.\n- Unequal cluster sizes (8..64) make cluster-size heterogeneity part of every cell.\nInformation availability: only design.csv is used; no violations possible (no observed data).\nExclusions: none (all 9 cells × 2000 replications simulated); ledger in out/exclusions.csv.\nEOF\nprintf 'step,rule,n_before,n_after,n_removed,rationale\\n1,none: all design cells x R replications simulated,18000,18000,0,No filtering applied; n_analysed = 9 cells x 2000 replications\\n' > exclusions.csv\necho \"- 2026-10-30 P1 passed: design audited, no exclusions; note G=5 Rademacher discreteness.\" >> state/log.md","description":"Write data audit and exclusion ledger"},"caller":{"type":"direct"}}],"container":null,"stop_reason":"tool_use","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":452,"cache_read_input_tokens":11343,"output_tokens":959,"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":452},"inference_geo":"global","iterations":[],"speed":"standard","fallback_credit":null},"input_transformations":[],"diagnostics":null,"context_management":null},"apiBlockIndex":0,"requestId":"req_011CgXt13cQwLHEG6EsheZBJ","requestRef":"497c2e8b-ac20-44ea-931a-d551168d51d1","requestedModel":"claude-opus-5-5","type":"assistant","uuid":"71729d80-a4b5-45e1-a07c-a01a33a05459","timestamp":"2026-10-30T05:36:39.884Z","effort":"medium","perTurnEffort":"medium","userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RT0019","sessionId":"47ce3250-2468-470f-8e8f-38dfc848301c","version":"2.1.314","gitBranch":"HEAD"}39{"parentUuid":"71729d80-a4b5-45e1-a07c-a01a33a05459","isSidechain":false,"promptId":"08889d0b-f553-4d4f-849c-8211aa68e575","type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_015gocsdUHnuUHtsQvqDqCHS","type":"tool_result","content":"(Bash completed with no output)","is_error":false}]},"uuid":"79717894-ef35-43a8-a9c8-ce571c777c9d","timestamp":"2026-10-30T05:36:39.925Z","toolUseResult":{"stdout":"","stderr":"","interrupted":false,"isImage":false,"noOutputExpected":false},"sandboxed":false,"sourceToolAssistantUUID":"71729d80-a4b5-45e1-a07c-a01a33a05459","permissionDecision":{"decision":"accept","source":"config","reasonType":"rule","ruleSource":"cliArg"},"userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RT0019/out","sessionId":"47ce3250-2468-470f-8e8f-38dfc848301c","version":"2.1.314","gitBranch":"HEAD"}40{"type":"api-request-blob","sessionId":"47ce3250-2468-470f-8e8f-38dfc848301c","hash":"2d36649dc79b71980681a4ae2c17e5446c3df9aabb768a0f1ed5f9d3ecdc3854","message":{"role":"assistant","content":[{"type":"tool_use","id":"toolu_015gocsdUHnuUHtsQvqDqCHS","name":"Bash","input":{"command":"cd /Users/simon-pierreboucher/wp29/runs/RT0019/out && cat > state/data_audit.md <<'EOF'\n# Data audit (P1)\n| file | SHA-256 | rows | cols |\n|---|---|---|---|\n| data/design.csv | f4ff004ed50d893eece1aaa7edced04d3c0439aaedf1d391ea4642a87d0ecc50 | 9 | 11 |\n| data/README.md | 73a6595909d92a69db281e283f193348ac244f255fcd5343ef42ae0353394e67 | (text) | — |\nHashes match bundle.json.\n\nSchema: cell_id(str C1..C9), G(int 5/10/30), icc(float 0/0.2/0.5), cluster_sizes(str '|'-sep ints), N(int), R=2000, B=399, seed(int 20261101..20261109), mu=0.0, total_variance=1.0, nominal_level=0.95. No dates (synthetic design).\nMissing values: 0. Duplicate rows: 0. Duplicate cell_id: 0.\nConsistency: for all 9 rows len(cluster_sizes)=G, sum(cluster_sizes)=N (128, 264, 864), and sizes follow 8,16,32,64 repeated. \n\nAnomalies / notes (not errors):\n- ICC=0.0 cells: u_g ~ N(0,0) is degenerate (u_g ≡ 0); handled by drawing u_g = sqrt(icc)*Z, which is exactly 0.\n- G=5 with Rademacher weights: only 2^5=32 distinct weight vectors, and |t*| is invariant to flipping all signs, so at most 16 distinct |t*| values; the B=399 bootstrap quantile is therefore highly discrete (known WCB limitation with very few clusters). Kept as specified; reported in data_issues.\n- Unequal cluster sizes (8..64) make cluster-size heterogeneity part of every cell.\nInformation availability: only design.csv is used; no violations possible (no observed data).\nExclusions: none (all 9 cells × 2000 replications simulated); ledger in out/exclusions.csv.\nEOF\nprintf 'step,rule,n_before,n_after,n_removed,rationale\\n1,none: all design cells x R replications simulated,18000,18000,0,No filtering applied; n_analysed = 9 cells x 2000 replications\\n' > exclusions.csv\necho \"- 2026-10-30 P1 passed: design audited, no exclusions; note G=5 Rademacher discreteness.\" >> state/log.md","description":"Write data audit and exclusion 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> analysis.py <<'EOF'\n\"\"\"T2-simulation: finite-sample coverage of CR0, CR1 and wild-cluster-bootstrap-t CIs for a mean.\nRun from workspace root:  ~/wp29/venv/bin/python out/analysis.py\nReads only data/design.csv; writes out/results.json, out/table1.csv, out/figure1.png.\nRNG: per cell numpy.random.default_rng(seed from design.csv). Within each replication, draws are\ntaken in the order: Z_u (G std normals), Z_e (N std normals), Rademacher weights (B x G, via rng.integers).\n\"\"\"\nimport json, os\nimport numpy as np, pandas as pd\nfrom scipy import stats\nimport matplotlib\nmatplotlib.use(\"Agg\")\nimport matplotlib.pyplot as plt\n\nROOT = os.path.dirname(os.path.dirname(os.path.abspath(__file__)))\nOUT = os.path.join(ROOT, \"out\")\ndesign = pd.read_csv(os.path.join(ROOT, \"data\", \"design.csv\"))\n\n\ndef key(est, G, icc):\n    return f\"{est}_G{int(G)}_icc{float(icc):.1f}\"\n\n\ndef run_cell(row):\n    G, icc, N, R, B = int(row.G), float(row.icc), int(row.N), int(row.R), int(row.B)\n    mu, level = float(row.mu), float(row.nominal_level)\n    n = np.array([int(x) for x in row.cluster_sizes.split(\"|\")])\n    assert len(n) == G and n.sum() == N\n    gid = np.repeat(np.arange(G), n)\n    var_exact = (icc * np.sum(n.astype(float) ** 2) + (1 - icc) * N) / N ** 2\n    z = stats.norm.ppf(0.5 + level / 2)            # 1.95996...; TASK specifies 1.96\n    z = 1.96\n    tcrit = stats.t.ppf(0.5 + level / 2, G - 1)\n    adj = G / (G - 1)\n    rng = np.random.default_rng(int(row.seed))\n    res = {k: np.empty(R) for k in [\"ybar\", \"v0\", \"v1\", \"q\", \"s2\"]}\n    for r in range(R):\n        u = np.sqrt(icc) * rng.standard_normal(G)\n        e = np.sqrt(1 - icc) * rng.standard_normal(N)\n        y = mu + u[gid] + e\n        ybar = y.mean()\n        ehat = y - ybar\n        S = np.bincount(gid, weights=ehat, minlength=G)     # cluster residual sums\n        v0 = np.sum(S ** 2) / N ** 2\n        v1 = adj * v0\n        # wild cluster bootstrap-t (unrestricted, Rademacher), vectorised over B\n        W = 2.0 * rng.integers(0, 2, size=(B, G)) - 1.0      # B x G\n        d = (W @ S) / N                                      # ybar* - ybar, length B\n        # bootstrap residual cluster sums: sum_i (w_g ehat_ig - d) = w_g S_g - n_g d\n        Sstar = W * S[None, :] - d[:, None] * n[None, :]\n        vstar = adj * np.sum(Sstar ** 2, axis=1) / N ** 2\n        tstar = np.abs(d) / np.sqrt(vstar)\n        q = np.quantile(tstar, 0.95)                         # numpy default (linear interpolation)\n        res[\"ybar\"][r], res[\"v0\"][r], res[\"v1\"][r], res[\"q\"][r] = ybar, v0, v1, q\n        res[\"s2\"][r] = ehat @ ehat / (N - 1)\n    hw = {\"cr0\": z * np.sqrt(res[\"v0\"]),\n          \"cr1\": tcrit * np.sqrt(res[\"v1\"]),\n          \"wcb\": res[\"q\"] * np.sqrt(res[\"v1\"]),\n          \"iid\": z * np.sqrt(res[\"s2\"] / N)}                # baseline: naive i.i.d. interval\n    out = {}\n    for est, h in hw.items():\n        cov = float(np.mean(np.abs(res[\"ybar\"] - mu) <= h))\n        out[est] = dict(coverage=cov, mc_se=float(np.sqrt(cov * (1 - cov) / R)),\n                        mean_hw=float(np.mean(h)))\n    out[\"cr0\"][\"rel_bias\"] = float(np.mean(res[\"v0\"]) / var_exact)\n    out[\"cr1\"][\"rel_bias\"] = float(np.mean(res[\"v1\"]) / var_exact)\n    out[\"iid\"][\"rel_bias\"] = float(np.mean(res[\"s2\"] / N) / var_exact)\n    # MC SE of the relative-bias estimate (sd of V-hat / sqrt(R) / Var exact)\n    out[\"cr0\"][\"rel_bias_se\"] = float(np.std(res[\"v0\"], ddof=1) / np.sqrt(R) / var_exact)\n    out[\"cr1\"][\"rel_bias_se\"] = float(np.std(res[\"v1\"], ddof=1) / np.sqrt(R) / var_exact)\n    out[\"meta\"] = dict(var_exact=var_exact, emp_var_ybar=float(np.var(res[\"ybar\"], ddof=1)),\n                       mean_ybar=float(np.mean(res[\"ybar\"])), tcrit=float(tcrit),\n                       mean_q=float(np.mean(res[\"q\"])), n_distinct_wcb_q=int(len(np.unique(np.round(res[\"q\"], 10)))))\n    return out\n\n\nresults = {\"task_id\": \"T2-simulation\"}\nRs, Bs = design.R.unique(), design.B.unique()\nassert len(Rs) == 1 and len(Bs) == 1\nR, B = int(Rs[0]), int(Bs[0])\ncov, mcse, rb, rb_se, base_cov, base_rb, cellmeta = {}, {}, {}, {}, {}, {}, {}\nrows = []\nfor _, row in design.iterrows():\n    o = run_cell(row)\n    for est in [\"cr0\", \"cr1\", \"wcb\"]:\n        k = key(est, row.G, row.icc)\n        cov[k], mcse[k] = o[est][\"coverage\"], o[est][\"mc_se\"]\n        if est != \"wcb\":\n            rb[k], rb_se[k] = o[est][\"rel_bias\"], o[est][\"rel_bias_se\"]\n        rows.append(dict(estimator=est, cell_id=row.cell_id, G=int(row.G), icc=float(row.icc),\n                         coverage=o[est][\"coverage\"], mc_se_coverage=o[est][\"mc_se\"],\n                         rel_bias_var=(o[est][\"rel_bias\"] if est != \"wcb\" else None),\n                         mean_ci_halfwidth=o[est][\"mean_hw\"], R=int(row.R)))\n    kb = key(\"iid\", row.G, row.icc)\n    base_cov[kb], base_rb[kb] = o[\"iid\"][\"coverage\"], o[\"iid\"][\"rel_bias\"]\n    cellmeta[row.cell_id] = o[\"meta\"]\n    print(f\"{row.cell_id} G={row.G} icc={row.icc}: cr0={o['cr0']['coverage']:.4f} \"\n          f\"cr1={o['cr1']['coverage']:.4f} wcb={o['wcb']['coverage']:.4f} iid={o['iid']['coverage']:.4f}\")\n\ntable = pd.DataFrame(rows)\ntable = table.sort_values([\"estimator\", \"cell_id\"]).reset_index(drop=True)\ntable.to_csv(os.path.join(OUT, \"table1.csv\"), index=False)\n\nfailure = sorted(k for k, v in cov.items() if v < 0.90)\nnominal = sorted(k for k, v in cov.items() if abs(v - 0.95) <= 2 * mcse[k])\navg_small = {est: float(np.mean([cov[key(est, 5, i)] for i in [0.0, 0.2, 0.5]])) for est in [\"cr0\", \"cr1\", \"wcb\"]}\nbest = max(avg_small, key=avg_small.get)\n\ndata_issues = [\n    {\"issue\": \"ICC = 0.0 cells imply u_g ~ N(0, 0), a degenerate distribution.\",\n     \"handling\": \"u_g drawn as sqrt(icc)*Z, which is identically 0 for icc = 0; DGP otherwise unchanged.\"},\n    {\"issue\": \"With G = 5 and Rademacher weights there are only 2^5 = 32 distinct weight vectors and |t*| is invariant to a global sign flip, so at most 16 distinct |t*| values exist; the B = 399 bootstrap quantile is highly discrete (draws repeat the same few values).\",\n     \"handling\": \"Kept exactly as specified (no Webb weights or enumeration); flagged as a limitation in the report.\"},\n    {\"issue\": \"'0.95 empirical quantile' of |t*| is not uniquely defined for finite B.\",\n     \"handling\": \"numpy.quantile default (linear interpolation, position 0.95*(B-1)); documented in report.\"},\n]\nresults.update({\n    \"R\": R, \"B\": B, \"n_cells\": int(len(design)),\n    \"coverage\": cov, \"mc_se_coverage\": mcse, \"rel_bias_var\": rb,\n    \"failure_cells\": failure, \"n_failure_cells\": len(failure), \"nominal_cells\": nominal,\n    \"best_procedure_small_G\": best,\n    \"feasibility\": \"complete\", \"feasibility_reason\": None,\n    \"data_issues\": data_issues,\n    \"n_input_rows\": {\"design.csv\": int(len(design))},\n    \"n_analysed\": int(len(design) * R),\n    \"random_seed\": int(design.loc[design.cell_id == \"C1\", \"seed\"].iloc[0]),\n    # supplementary keys\n    \"avg_coverage_small_G\": avg_small,\n    \"mc_se_rel_bias_var\": rb_se,\n    \"baseline_iid_coverage\": base_cov,\n    \"baseline_iid_rel_bias_var\": base_rb,\n    \"cell_diagnostics\": cellmeta,\n    \"seeds\": {r.cell_id: int(r.seed) for _, r in design.iterrows()},\n})\nwith open(os.path.join(OUT, \"results.json\"), \"w\") as f:\n    json.dump(results, f, indent=2)\n\n# Figure\nfig, axes = plt.subplots(1, 3, figsize=(13, 4.2), sharey=True)\nGs = sorted(design.G.unique())\nfor ax, icc in zip(axes, [0.0, 0.2, 0.5]):\n    for est, mk in [(\"cr0\", \"o\"), (\"cr1\", \"s\"), (\"wcb\", \"^\")]:\n        p = np.array([cov[key(est, G, icc)] for G in Gs])\n        s = np.array([mcse[key(est, G, icc)] for G in Gs])\n        ax.errorbar(Gs, p, yerr=1.96 * s, marker=mk, capsize=3, label=est.upper())\n    ax.axhline(0.95, color=\"k\", ls=\"--\", lw=1, label=\"nominal 0.95\")\n    ax.axhline(0.90, color=\"r\", ls=\":\", lw=1, label=\"failure line 0.90\")\n    ax.set_xscale(\"log\"); ax.set_xticks(Gs); ax.set_xticklabels([str(g) for g in Gs])\n    ax.set_title(f\"ICC = {icc:.1f}\"); ax.set_xlabel(\"number of clusters G\")\naxes[0].set_ylabel(\"coverage of nominal 95% CI (±1.96 MC SE)\")\naxes[0].legend(fontsize=8, loc=\"lower right\")\nfig.suptitle(f\"Coverage of cluster-robust CIs for a mean (R={R}, B={B})\")\nfig.tight_layout()\nfig.savefig(os.path.join(OUT, \"figure1.png\"), dpi=130)\nprint(\"failure cells:\", failure)\nprint(\"best procedure (G=5):\", best, avg_small)\nEOF\ncd .. && time ~/wp29/venv/bin/python out/analysis.py","description":"Write and run analysis 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G=5 icc=0.0: cr0=0.7805 cr1=0.9115 wcb=0.8550 iid=0.9405\nC2 G=5 icc=0.2: cr0=0.7210 cr1=0.8845 wcb=0.8540 iid=0.4600\nC3 G=5 icc=0.5: cr0=0.7170 cr1=0.8755 wcb=0.8490 iid=0.2865\nC4 G=10 icc=0.0: cr0=0.8965 cr1=0.9435 wcb=0.9360 iid=0.9570\nC5 G=10 icc=0.2: cr0=0.8570 cr1=0.9155 wcb=0.9295 iid=0.4880\nC6 G=10 icc=0.5: cr0=0.8530 cr1=0.9065 wcb=0.9325 iid=0.3125\nC7 G=30 icc=0.0: cr0=0.9275 cr1=0.9400 wcb=0.9430 iid=0.9515\nC8 G=30 icc=0.2: cr0=0.9210 cr1=0.9350 wcb=0.9460 iid=0.4850\nC9 G=30 icc=0.5: cr0=0.9165 cr1=0.9300 wcb=0.9425 iid=0.2985\nfailure cells: ['cr0_G10_icc0.0', 'cr0_G10_icc0.2', 'cr0_G10_icc0.5', 'cr0_G5_icc0.0', 'cr0_G5_icc0.2', 'cr0_G5_icc0.5', 'cr1_G5_icc0.2', 'cr1_G5_icc0.5', 'wcb_G5_icc0.0', 'wcb_G5_icc0.2', 'wcb_G5_icc0.5']\nbest procedure (G=5): cr1 {'cr0': 0.7395, 'cr1': 0.8905, 'wcb': 0.8526666666666666}\n~/wp29/venv/bin/python out/analysis.py  2,07s user 0,07s system 99% cpu 2,142 total","is_error":false}]},"uuid":"d10e0a4f-c30b-43ba-82c5-e2dbaa9aea4c","timestamp":"2026-10-30T05:37:18.201Z","toolUseResult":{"stdout":"C1 G=5 icc=0.0: cr0=0.7805 cr1=0.9115 wcb=0.8550 iid=0.9405\nC2 G=5 icc=0.2: cr0=0.7210 cr1=0.8845 wcb=0.8540 iid=0.4600\nC3 G=5 icc=0.5: cr0=0.7170 cr1=0.8755 wcb=0.8490 iid=0.2865\nC4 G=10 icc=0.0: cr0=0.8965 cr1=0.9435 wcb=0.9360 iid=0.9570\nC5 G=10 icc=0.2: cr0=0.8570 cr1=0.9155 wcb=0.9295 iid=0.4880\nC6 G=10 icc=0.5: cr0=0.8530 cr1=0.9065 wcb=0.9325 iid=0.3125\nC7 G=30 icc=0.0: cr0=0.9275 cr1=0.9400 wcb=0.9430 iid=0.9515\nC8 G=30 icc=0.2: cr0=0.9210 cr1=0.9350 wcb=0.9460 iid=0.4850\nC9 G=30 icc=0.5: cr0=0.9165 cr1=0.9300 wcb=0.9425 iid=0.2985\nfailure cells: ['cr0_G10_icc0.0', 'cr0_G10_icc0.2', 'cr0_G10_icc0.5', 'cr0_G5_icc0.0', 'cr0_G5_icc0.2', 'cr0_G5_icc0.5', 'cr1_G5_icc0.2', 'cr1_G5_icc0.5', 'wcb_G5_icc0.0', 'wcb_G5_icc0.2', 'wcb_G5_icc0.5']\nbest procedure (G=5): cr1 {'cr0': 0.7395, 'cr1': 0.8905, 'wcb': 0.8526666666666666}\n~/wp29/venv/bin/python out/analysis.py  2,07s user 0,07s system 99% cpu 2,142 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> analysis.py <<'EOF'\n\"\"\"T2-simulation: finite-sample coverage of CR0, CR1 and wild-cluster-bootstrap-t CIs for a mean.\nRun from workspace root:  ~/wp29/venv/bin/python out/analysis.py\nReads only data/design.csv; writes out/results.json, out/table1.csv, out/figure1.png.\nRNG: per cell numpy.random.default_rng(seed from design.csv). Within each replication, draws are\ntaken in the order: Z_u (G std normals), Z_e (N std normals), Rademacher weights (B x G, via rng.integers).\n\"\"\"\nimport json, os\nimport numpy as np, pandas as pd\nfrom scipy import stats\nimport matplotlib\nmatplotlib.use(\"Agg\")\nimport matplotlib.pyplot as plt\n\nROOT = os.path.dirname(os.path.dirname(os.path.abspath(__file__)))\nOUT = os.path.join(ROOT, \"out\")\ndesign = pd.read_csv(os.path.join(ROOT, \"data\", \"design.csv\"))\n\n\ndef key(est, G, icc):\n    return f\"{est}_G{int(G)}_icc{float(icc):.1f}\"\n\n\ndef run_cell(row):\n    G, icc, N, R, B = int(row.G), float(row.icc), int(row.N), int(row.R), int(row.B)\n    mu, level = float(row.mu), float(row.nominal_level)\n    n = np.array([int(x) for x in row.cluster_sizes.split(\"|\")])\n    assert len(n) == G and n.sum() == N\n    gid = np.repeat(np.arange(G), n)\n    var_exact = (icc * np.sum(n.astype(float) ** 2) + (1 - icc) * N) / N ** 2\n    z = stats.norm.ppf(0.5 + level / 2)            # 1.95996...; TASK specifies 1.96\n    z = 1.96\n    tcrit = stats.t.ppf(0.5 + level / 2, G - 1)\n    adj = G / (G - 1)\n    rng = np.random.default_rng(int(row.seed))\n    res = {k: np.empty(R) for k in [\"ybar\", \"v0\", \"v1\", \"q\", \"s2\"]}\n    for r in range(R):\n        u = np.sqrt(icc) * rng.standard_normal(G)\n        e = np.sqrt(1 - icc) * rng.standard_normal(N)\n        y = mu + u[gid] + e\n        ybar = y.mean()\n        ehat = y - ybar\n        S = np.bincount(gid, weights=ehat, minlength=G)     # cluster residual sums\n        v0 = np.sum(S ** 2) / N ** 2\n        v1 = adj * v0\n        # wild cluster bootstrap-t (unrestricted, Rademacher), vectorised over B\n        W = 2.0 * rng.integers(0, 2, size=(B, G)) - 1.0      # B x G\n        d = (W @ S) / N                                      # ybar* - ybar, length B\n        # bootstrap residual cluster sums: sum_i (w_g ehat_ig - d) = w_g S_g - n_g d\n        Sstar = W * S[None, :] - d[:, None] * n[None, :]\n        vstar = adj * np.sum(Sstar ** 2, axis=1) / N ** 2\n        tstar = np.abs(d) / np.sqrt(vstar)\n        q = np.quantile(tstar, 0.95)                         # numpy default (linear interpolation)\n        res[\"ybar\"][r], res[\"v0\"][r], res[\"v1\"][r], res[\"q\"][r] = ybar, v0, v1, q\n        res[\"s2\"][r] = ehat @ ehat / (N - 1)\n    hw = {\"cr0\": z * np.sqrt(res[\"v0\"]),\n          \"cr1\": tcrit * np.sqrt(res[\"v1\"]),\n          \"wcb\": res[\"q\"] * np.sqrt(res[\"v1\"]),\n          \"iid\": z * np.sqrt(res[\"s2\"] / N)}                # baseline: naive i.i.d. interval\n    out = {}\n    for est, h in hw.items():\n        cov = float(np.mean(np.abs(res[\"ybar\"] - mu) <= h))\n        out[est] = dict(coverage=cov, mc_se=float(np.sqrt(cov * (1 - cov) / R)),\n                        mean_hw=float(np.mean(h)))\n    out[\"cr0\"][\"rel_bias\"] = float(np.mean(res[\"v0\"]) / var_exact)\n    out[\"cr1\"][\"rel_bias\"] = float(np.mean(res[\"v1\"]) / var_exact)\n    out[\"iid\"][\"rel_bias\"] = float(np.mean(res[\"s2\"] / N) / var_exact)\n    # MC SE of the relative-bias estimate (sd of V-hat / sqrt(R) / Var exact)\n    out[\"cr0\"][\"rel_bias_se\"] = float(np.std(res[\"v0\"], ddof=1) / np.sqrt(R) / var_exact)\n    out[\"cr1\"][\"rel_bias_se\"] = float(np.std(res[\"v1\"], ddof=1) / np.sqrt(R) / var_exact)\n    out[\"meta\"] = dict(var_exact=var_exact, emp_var_ybar=float(np.var(res[\"ybar\"], ddof=1)),\n                       mean_ybar=float(np.mean(res[\"ybar\"])), tcrit=float(tcrit),\n                       mean_q=float(np.mean(res[\"q\"])), n_distinct_wcb_q=int(len(np.unique(np.round(res[\"q\"], 10)))))\n    return out\n\n\nresults = {\"task_id\": \"T2-simulation\"}\nRs, Bs = design.R.unique(), design.B.unique()\nassert len(Rs) == 1 and len(Bs) == 1\nR, B = int(Rs[0]), int(Bs[0])\ncov, mcse, rb, rb_se, base_cov, base_rb, cellmeta = {}, {}, {}, {}, {}, {}, {}\nrows = []\nfor _, row in design.iterrows():\n    o = run_cell(row)\n    for est in [\"cr0\", \"cr1\", \"wcb\"]:\n        k = key(est, row.G, row.icc)\n        cov[k], mcse[k] = o[est][\"coverage\"], o[est][\"mc_se\"]\n        if est != \"wcb\":\n            rb[k], rb_se[k] = o[est][\"rel_bias\"], o[est][\"rel_bias_se\"]\n        rows.append(dict(estimator=est, cell_id=row.cell_id, G=int(row.G), icc=float(row.icc),\n                         coverage=o[est][\"coverage\"], mc_se_coverage=o[est][\"mc_se\"],\n                         rel_bias_var=(o[est][\"rel_bias\"] if est != \"wcb\" else None),\n                         mean_ci_halfwidth=o[est][\"mean_hw\"], R=int(row.R)))\n    kb = key(\"iid\", row.G, row.icc)\n    base_cov[kb], base_rb[kb] = o[\"iid\"][\"coverage\"], o[\"iid\"][\"rel_bias\"]\n    cellmeta[row.cell_id] = o[\"meta\"]\n    print(f\"{row.cell_id} G={row.G} icc={row.icc}: cr0={o['cr0']['coverage']:.4f} \"\n          f\"cr1={o['cr1']['coverage']:.4f} wcb={o['wcb']['coverage']:.4f} iid={o['iid']['coverage']:.4f}\")\n\ntable = pd.DataFrame(rows)\ntable = table.sort_values([\"estimator\", \"cell_id\"]).reset_index(drop=True)\ntable.to_csv(os.path.join(OUT, \"table1.csv\"), index=False)\n\nfailure = sorted(k for k, v in cov.items() if v < 0.90)\nnominal = sorted(k for k, v in cov.items() if abs(v - 0.95) <= 2 * mcse[k])\navg_small = {est: float(np.mean([cov[key(est, 5, i)] for i in [0.0, 0.2, 0.5]])) for est in [\"cr0\", \"cr1\", \"wcb\"]}\nbest = max(avg_small, key=avg_small.get)\n\ndata_issues = [\n    {\"issue\": \"ICC = 0.0 cells imply u_g ~ N(0, 0), a degenerate distribution.\",\n     \"handling\": \"u_g drawn as sqrt(icc)*Z, which is identically 0 for icc = 0; DGP otherwise unchanged.\"},\n    {\"issue\": \"With G = 5 and Rademacher weights there are only 2^5 = 32 distinct weight vectors and |t*| is invariant to a global sign flip, so at most 16 distinct |t*| values exist; the B = 399 bootstrap quantile is highly discrete (draws repeat the same few values).\",\n     \"handling\": \"Kept exactly as specified (no Webb weights or enumeration); flagged as a limitation in the report.\"},\n    {\"issue\": \"'0.95 empirical quantile' of |t*| is not uniquely defined for finite B.\",\n     \"handling\": \"numpy.quantile default (linear interpolation, position 0.95*(B-1)); documented in report.\"},\n]\nresults.update({\n    \"R\": R, \"B\": B, \"n_cells\": int(len(design)),\n    \"coverage\": cov, \"mc_se_coverage\": mcse, \"rel_bias_var\": rb,\n    \"failure_cells\": failure, \"n_failure_cells\": len(failure), \"nominal_cells\": nominal,\n    \"best_procedure_small_G\": best,\n    \"feasibility\": \"complete\", \"feasibility_reason\": None,\n    \"data_issues\": data_issues,\n    \"n_input_rows\": {\"design.csv\": int(len(design))},\n    \"n_analysed\": int(len(design) * R),\n    \"random_seed\": int(design.loc[design.cell_id == \"C1\", \"seed\"].iloc[0]),\n    # supplementary keys\n    \"avg_coverage_small_G\": avg_small,\n    \"mc_se_rel_bias_var\": rb_se,\n    \"baseline_iid_coverage\": base_cov,\n    \"baseline_iid_rel_bias_var\": base_rb,\n    \"cell_diagnostics\": cellmeta,\n    \"seeds\": {r.cell_id: int(r.seed) for _, r in design.iterrows()},\n})\nwith open(os.path.join(OUT, \"results.json\"), \"w\") as f:\n    json.dump(results, f, indent=2)\n\n# Figure\nfig, axes = plt.subplots(1, 3, figsize=(13, 4.2), sharey=True)\nGs = sorted(design.G.unique())\nfor ax, icc in zip(axes, [0.0, 0.2, 0.5]):\n    for est, mk in [(\"cr0\", \"o\"), (\"cr1\", \"s\"), (\"wcb\", \"^\")]:\n        p = np.array([cov[key(est, G, icc)] for G in Gs])\n        s = np.array([mcse[key(est, G, icc)] for G in Gs])\n        ax.errorbar(Gs, p, yerr=1.96 * s, marker=mk, capsize=3, label=est.upper())\n    ax.axhline(0.95, color=\"k\", ls=\"--\", lw=1, label=\"nominal 0.95\")\n    ax.axhline(0.90, color=\"r\", ls=\":\", lw=1, label=\"failure line 0.90\")\n    ax.set_xscale(\"log\"); ax.set_xticks(Gs); ax.set_xticklabels([str(g) for g in Gs])\n    ax.set_title(f\"ICC = {icc:.1f}\"); ax.set_xlabel(\"number of clusters G\")\naxes[0].set_ylabel(\"coverage of nominal 95% CI (±1.96 MC SE)\")\naxes[0].legend(fontsize=8, loc=\"lower right\")\nfig.suptitle(f\"Coverage of cluster-robust CIs for a mean (R={R}, B={B})\")\nfig.tight_layout()\nfig.savefig(os.path.join(OUT, \"figure1.png\"), dpi=130)\nprint(\"failure cells:\", failure)\nprint(\"best procedure (G=5):\", best, avg_small)\nEOF\ncd .. && time ~/wp29/venv/bin/python out/analysis.py","description":"Write and run analysis 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0.6616434596045324,\n\"cr1_G5_icc0.0\": 0.8270543245056654,\n\"cr0_G5_icc0.2\": 0.5313958571125164,\n\"cr1_G5_icc0.2\": 0.6642448213906453,\n\"cr0_G5_icc0.5\": 0.48335023384407516,\n\"cr1_G5_icc0.5\": 0.6041877923050938,\n\"cr0_G10_icc0.0\": 0.8558047169939658,\n\"cr1_G10_icc0.0\": 0.9508941299932954,\n\"cr0_G10_icc0.2\": 0.7624366224508259,\n\"cr1_G10_icc0.2\": 0.8471518027231398,\n\"cr0_G10_icc0.5\": 0.7737432482368367,\n\"cr1_G10_icc0.5\": 0.859714720263152,\n\"cr0_G30_icc0.0\": 0.943796503270819,\n\"cr1_G30_icc0.0\": 0.9763412102801576,\n\"cr0_G30_icc0.2\": 0.9313668684580917,\n\"cr1_G30_icc0.2\": 0.9634829673704397,\n\"cr0_G30_icc0.5\": 0.9252598675851053,\n\"cr1_G30_icc0.5\": 0.9571653802604538\n}\n{\"cr0_G5_icc0.0\": 0.012963511182344976, \"cr1_G5_icc0.0\": 0.01620438897793122, \"cr0_G5_icc0.2\": 0.012975387163286884, \"cr1_G5_icc0.2\": 0.016219233954108608, \"cr0_G5_icc0.5\": 0.011854909267984598, \"cr1_G5_icc0.5\": 0.01481863658498075, \"cr0_G10_icc0.0\": 0.011793478824212352, \"cr1_G10_icc0.0\": 0.013103865360235948, \"cr0_G10_icc0.2\": 0.012887411160981703, \"cr1_G10_icc0.2\": 0.014319345734424112, \"cr0_G10_icc0.5\": 0.014100595197540216, \"cr1_G10_icc0.5\": 0.015667327997266903, \"cr0_G30_icc0.0\": 0.006765949452231232, \"cr1_G30_icc0.0\": 0.006999258054032309, \"cr0_G30_icc0.2\": 0.00853308139600021, \"cr1_G30_icc0.2\": 0.008827325582069187, \"cr0_G30_icc0.5\": 0.008700682520806436, \"cr1_G30_icc0.5\": 0.009000706056006657}\n{\n\"C1\": {\n\"var_exact\": 0.0078125,\n\"emp_var_ybar\": 0.008088486730047813,\n\"mean_ybar\": 0.00028867307788429486,\n\"tcrit\": 2.7764451051977987,\n\"mean_q\": 3.058237347731799,\n\"n_distinct_wcb_q\": 2000\n},\n\"C2\": {\n\"var_exact\": 0.0734375,\n\"emp_var_ybar\": 0.07818095367838697,\n\"mean_ybar\": 0.010497786660162045,\n\"tcrit\": 2.7764451051977987,\n\"mean_q\": 3.95345460040353,\n\"n_distinct_wcb_q\": 2000\n},\n\"C3\": {\n\"var_exact\": 0.171875,\n\"emp_var_ybar\": 0.16265026991742373,\n\"mean_ybar\": -0.005763243574855418,\n\"tcrit\": 2.7764451051977987,\n\"mean_q\": 4.057367385569531,\n\"n_distinct_wcb_q\": 2000\n},\n\"C4\": {\n\"var_exact\": 0.003787878787878788,\n\"emp_var_ybar\": 0.0035362244420084166,\n\"mean_ybar\": -8.481537588492616e-05,\n\"tcrit\": 2.262157162854099,\n\"mean_q\": 2.457985884403817,\n\"n_distinct_wcb_q\": 2000\n},\n\"C5\": {\n\"var_exact\": 0.035169880624426074,\n\"emp_var_ybar\": 0.03406304358390313,\n\"mean_ybar\": 0.004033474563745705,\n\"tcrit\": 2.262157162854099,\n\"mean_q\": 2.753520301245196,\n\"n_distinct_wcb_q\": 2000\n},\n\"C6\": {\n\"var_exact\": 0.08224288337924701,\n\"emp_var_ybar\": 0.080563566169808,\n\"mean_ybar\": -0.0013932598375193423,\n\"tcrit\": 2.262157162854099,\n\"mean_q\": 2.8281991199760155,\n\"n_distinct_wcb_q\": 2000\n},\n\"C7\": {\n\"var_exact\": 0.0011574074074074073,\n\"emp_var_ybar\": 0.0011557426105430798,\n\"mean_ybar\": -0.000782799084647925,\n\"tcrit\": 2.0452296421327034,\n\"mean_q\": 2.093352473864349,\n\"n_distinct_wcb_q\": 2000\n},\n\"C8\": {\n\"var_exact\": 0.011213991769547326,\n\"emp_var_ybar\": 0.010533848298136778,\n\"mean_ybar\": 0.0021016344076126483,\n\"tcrit\": 2.0452296421327034,\n\"mean_q\": 2.1732383487014815,\n\"n_distinct_wcb_q\": 2000\n},\n\"C9\": {\n\"var_exact\": 0.0262988683127572,\n\"emp_var_ybar\": 0.027362497147835698,\n\"mean_ybar\": -0.0024768417619560603,\n\"tcrit\": 2.0452296421327034,\n\"mean_q\": 2.172088990939701,\n\"n_distinct_wcb_q\": 2000\n}\n}\n['cr1_G10_icc0.0', 'cr1_G30_icc0.0', 'wcb_G30_icc0.0', 'wcb_G30_icc0.2', 'wcb_G30_icc0.5']\n   estimator cell_id   G  icc  coverage  mc_se_coverage  rel_bias_var  mean_ci_halfwidth     R\n0        cr0      C1   5  0.0    0.7805        0.009255      0.661643           0.129684  2000\n1        cr0      C2   5  0.2    0.7210        0.010029      0.531396           0.342268  2000\n2        cr0      C3   5  0.5    0.7170        0.010073      0.483350           0.498894  2000\n3        cr0      C4  10  0.0    0.8965        0.006811      0.855805           0.106852  2000\n4        cr0      C5  10  0.2    0.8570        0.007828      0.762437           0.301309  2000\n5        cr0      C6  10  0.5    0.8530        0.007918      0.773743           0.461422  2000\n6        cr0      C7  30  0.0    0.9275        0.005798      0.943797           0.063969  2000\n7        cr0      C8  30  0.2    0.9210        0.006032      0.931367           0.196303  2000\n8        cr0      C9  30  0.5    0.9165        0.006186      0.925260           0.299237  2000\n9        cr1      C1   5  0.0    0.9115        0.006351      0.827054           0.205387  2000\n10       cr1      C2   5  0.2    0.8845        0.007147      0.664245           0.542069  2000\n11       cr1      C3   5  0.5    0.8755        0.007382      0.604188           0.790125  2000\n12       cr1      C4  10  0.0    0.9435        0.005163      0.950894           0.129995  2000\n13       cr1      C5  10  0.2    0.9155        0.006219      0.847152           0.366571  2000\n14       cr1      C6  10  0.5    0.9065        0.006510      0.859715           0.561363  2000\n15       cr1      C7  30  0.0    0.9400        0.005310      0.976341           0.067892  2000\n16       cr1      C8  30  0.2    0.9350        0.005512      0.963483           0.208341  2000\n17       cr1      C9  30  0.5    0.9300        0.005705      0.957165           0.317587  2000\n18       wcb      C1   5  0.0    0.8550        0.007873           NaN           0.248870  2000\n19       wcb      C2   5  0.2    0.8540        0.007896           NaN           0.906131  2000\n20       wcb      C3   5  0.5    0.8490        0.008006           NaN           1.362462  2000\n21       wcb      C4  10  0.0    0.9360        0.005473           NaN           0.144861  2000\n22       wcb      C5  10  0.2    0.9295        0.005724           NaN           0.460234  2000\n23       wcb      C6  10  0.5    0.9325        0.005610           NaN           0.722084  2000\n24       wcb      C7  30  0.0    0.9430        0.005184           NaN           0.069647  2000\n25       wcb      C8  30  0.2    0.9460        0.005054           NaN           0.222080  2000\n26       wcb      C9  30  0.5    0.9425        0.005205           NaN           0.338133  2000","is_error":false}]},"uuid":"f565891d-6d63-415e-acdc-8985f1ec7c8b","timestamp":"2026-10-30T05:37:32.233Z","toolUseResult":{"stdout":"SAME_JSON\nSAME_TABLE\n{\n\"cr0_G5_icc0.0\": 0.6616434596045324,\n\"cr1_G5_icc0.0\": 0.8270543245056654,\n\"cr0_G5_icc0.2\": 0.5313958571125164,\n\"cr1_G5_icc0.2\": 0.6642448213906453,\n\"cr0_G5_icc0.5\": 0.48335023384407516,\n\"cr1_G5_icc0.5\": 0.6041877923050938,\n\"cr0_G10_icc0.0\": 0.8558047169939658,\n\"cr1_G10_icc0.0\": 0.9508941299932954,\n\"cr0_G10_icc0.2\": 0.7624366224508259,\n\"cr1_G10_icc0.2\": 0.8471518027231398,\n\"cr0_G10_icc0.5\": 0.7737432482368367,\n\"cr1_G10_icc0.5\": 0.859714720263152,\n\"cr0_G30_icc0.0\": 0.943796503270819,\n\"cr1_G30_icc0.0\": 0.9763412102801576,\n\"cr0_G30_icc0.2\": 0.9313668684580917,\n\"cr1_G30_icc0.2\": 0.9634829673704397,\n\"cr0_G30_icc0.5\": 0.9252598675851053,\n\"cr1_G30_icc0.5\": 0.9571653802604538\n}\n{\"cr0_G5_icc0.0\": 0.012963511182344976, \"cr1_G5_icc0.0\": 0.01620438897793122, \"cr0_G5_icc0.2\": 0.012975387163286884, \"cr1_G5_icc0.2\": 0.016219233954108608, \"cr0_G5_icc0.5\": 0.011854909267984598, \"cr1_G5_icc0.5\": 0.01481863658498075, \"cr0_G10_icc0.0\": 0.011793478824212352, \"cr1_G10_icc0.0\": 0.013103865360235948, \"cr0_G10_icc0.2\": 0.012887411160981703, \"cr1_G10_icc0.2\": 0.014319345734424112, \"cr0_G10_icc0.5\": 0.014100595197540216, \"cr1_G10_icc0.5\": 0.015667327997266903, \"cr0_G30_icc0.0\": 0.006765949452231232, \"cr1_G30_icc0.0\": 0.006999258054032309, \"cr0_G30_icc0.2\": 0.00853308139600021, \"cr1_G30_icc0.2\": 0.008827325582069187, \"cr0_G30_icc0.5\": 0.008700682520806436, \"cr1_G30_icc0.5\": 0.009000706056006657}\n{\n\"C1\": {\n\"var_exact\": 0.0078125,\n\"emp_var_ybar\": 0.008088486730047813,\n\"mean_ybar\": 0.00028867307788429486,\n\"tcrit\": 2.7764451051977987,\n\"mean_q\": 3.058237347731799,\n\"n_distinct_wcb_q\": 2000\n},\n\"C2\": {\n\"var_exact\": 0.0734375,\n\"emp_var_ybar\": 0.07818095367838697,\n\"mean_ybar\": 0.010497786660162045,\n\"tcrit\": 2.7764451051977987,\n\"mean_q\": 3.95345460040353,\n\"n_distinct_wcb_q\": 2000\n},\n\"C3\": {\n\"var_exact\": 0.171875,\n\"emp_var_ybar\": 0.16265026991742373,\n\"mean_ybar\": -0.005763243574855418,\n\"tcrit\": 2.7764451051977987,\n\"mean_q\": 4.057367385569531,\n\"n_distinct_wcb_q\": 2000\n},\n\"C4\": {\n\"var_exact\": 0.003787878787878788,\n\"emp_var_ybar\": 0.0035362244420084166,\n\"mean_ybar\": -8.481537588492616e-05,\n\"tcrit\": 2.262157162854099,\n\"mean_q\": 2.457985884403817,\n\"n_distinct_wcb_q\": 2000\n},\n\"C5\": {\n\"var_exact\": 0.035169880624426074,\n\"emp_var_ybar\": 0.03406304358390313,\n\"mean_ybar\": 0.004033474563745705,\n\"tcrit\": 2.262157162854099,\n\"mean_q\": 2.753520301245196,\n\"n_distinct_wcb_q\": 2000\n},\n\"C6\": {\n\"var_exact\": 0.08224288337924701,\n\"emp_var_ybar\": 0.080563566169808,\n\"mean_ybar\": -0.0013932598375193423,\n\"tcrit\": 2.262157162854099,\n\"mean_q\": 2.8281991199760155,\n\"n_distinct_wcb_q\": 2000\n},\n\"C7\": {\n\"var_exact\": 0.0011574074074074073,\n\"emp_var_ybar\": 0.0011557426105430798,\n\"mean_ybar\": -0.000782799084647925,\n\"tcrit\": 2.0452296421327034,\n\"mean_q\": 2.093352473864349,\n\"n_distinct_wcb_q\": 2000\n},\n\"C8\": {\n\"var_exact\": 0.011213991769547326,\n\"emp_var_ybar\": 0.010533848298136778,\n\"mean_ybar\": 0.0021016344076126483,\n\"tcrit\": 2.0452296421327034,\n\"mean_q\": 2.1732383487014815,\n\"n_distinct_wcb_q\": 2000\n},\n\"C9\": {\n\"var_exact\": 0.0262988683127572,\n\"emp_var_ybar\": 0.027362497147835698,\n\"mean_ybar\": -0.0024768417619560603,\n\"tcrit\": 2.0452296421327034,\n\"mean_q\": 2.172088990939701,\n\"n_distinct_wcb_q\": 2000\n}\n}\n['cr1_G10_icc0.0', 'cr1_G30_icc0.0', 'wcb_G30_icc0.0', 'wcb_G30_icc0.2', 'wcb_G30_icc0.5']\n   estimator cell_id   G  icc  coverage  mc_se_coverage  rel_bias_var  mean_ci_halfwidth     R\n0        cr0      C1   5  0.0    0.7805        0.009255      0.661643           0.129684  2000\n1        cr0      C2   5  0.2    0.7210        0.010029      0.531396           0.342268  2000\n2        cr0      C3   5  0.5    0.7170        0.010073      0.483350           0.498894  2000\n3        cr0      C4  10  0.0    0.8965        0.006811      0.855805           0.106852  2000\n4        cr0      C5  10  0.2    0.8570        0.007828      0.762437           0.301309  2000\n5        cr0      C6  10  0.5    0.8530        0.007918      0.773743           0.461422  2000\n6        cr0      C7  30  0.0    0.9275        0.005798      0.943797           0.063969  2000\n7        cr0      C8  30  0.2    0.9210        0.006032      0.931367           0.196303  2000\n8        cr0      C9  30  0.5    0.9165        0.006186      0.925260           0.299237  2000\n9        cr1      C1   5  0.0    0.9115        0.006351      0.827054           0.205387  2000\n10       cr1      C2   5  0.2    0.8845        0.007147      0.664245           0.542069  2000\n11       cr1      C3   5  0.5    0.8755        0.007382      0.604188           0.790125  2000\n12       cr1      C4  10  0.0    0.9435        0.005163      0.950894           0.129995  2000\n13       cr1      C5  10  0.2    0.9155        0.006219      0.847152           0.366571  2000\n14       cr1      C6  10  0.5    0.9065        0.006510      0.859715           0.561363  2000\n15       cr1      C7  30  0.0    0.9400        0.005310      0.976341           0.067892  2000\n16       cr1      C8  30  0.2    0.9350        0.005512      0.963483           0.208341  2000\n17       cr1      C9  30  0.5    0.9300        0.005705      0.957165           0.317587  2000\n18       wcb      C1   5  0.0    0.8550        0.007873           NaN           0.248870  2000\n19       wcb      C2   5  0.2    0.8540        0.007896           NaN           0.906131  2000\n20       wcb      C3   5  0.5    0.8490        0.008006           NaN           1.362462  2000\n21       wcb      C4  10  0.0    0.9360        0.005473           NaN           0.144861  2000\n22       wcb      C5  10  0.2    0.9295        0.005724           NaN           0.460234  2000\n23       wcb      C6  10  0.5    0.9325        0.005610           NaN           0.722084  2000\n24       wcb      C7  30  0.0    0.9430        0.005184           NaN           0.069647  2000\n25       wcb      C8  30  0.2    0.9460        0.005054           NaN           0.222080  2000\n26       wcb      C9  30  0.5    0.9425        0.005205           NaN           0.338133  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- <<'EOF'\nimport re\np='out/analysis.py'; s=open(p).read()\ns=s.replace(\"    z = stats.norm.ppf(0.5 + level / 2)            # 1.95996...; TASK specifies 1.96\\n    z = 1.96\\n\",\"    z = 1.96                                         # as specified in TASK.md\\n\")\nopen(p,'w').write(s)\nEOF\ncp out/results.json /tmp/r1.json; md5 -q out/table1.csv > /tmp/t1\nrm out/results.json out/table1.csv out/figure1.png; ~/wp29/venv/bin/python out/analysis.py >/dev/null\ncmp out/results.json /tmp/r1.json && echo SAME_JSON; [ \"$(md5 -q out/table1.csv)\" = \"$(cat /tmp/t1)\" ] && echo SAME_TABLE\n~/wp29/venv/bin/python - <<'EOF'\nimport json,pandas as pd,numpy as np\nr=json.load(open('out/results.json'))\nprint(json.dumps(r['rel_bias_var'],indent=0)); print(json.dumps(r['mc_se_rel_bias_var']))\nprint(json.dumps(r['cell_diagnostics'],indent=0))\nprint(r['nominal_cells'])\nt=pd.read_csv('out/table1.csv'); print(t.to_string())\nEOF","description":"Clean z line, rerun from clean state, inspect 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> out/state/verify_checks.py <<'EOF'\n# Verification checks for P4 (reads out/results.json, out/table1.csv, data/design.csv)\nimport json, numpy as np, pandas as pd\nr = json.load(open('out/results.json')); t = pd.read_csv('out/table1.csv'); d = pd.read_csv('data/design.csv')\nok = lambda c, m: print((\"PASS \" if c else \"FAIL \") + m)\nok(len(t) == 27 and set(t.estimator) == {'cr0','cr1','wcb'}, \"table1 has 27 rows, 3 estimators\")\nok(len(r['coverage']) == 27 and len(r['mc_se_coverage']) == 27 and len(r['rel_bias_var']) == 18, \"key counts 27/27/18\")\nok(t[t.estimator=='wcb'].rel_bias_var.isna().all(), \"rel_bias_var empty for wcb\")\nok(all(0 <= v <= 1 for v in r['coverage'].values()), \"coverages in [0,1]\")\nok(all(v > 0 for v in r['mc_se_coverage'].values()), \"MC SEs positive\")\nok(all(abs(r['mc_se_coverage'][k] - np.sqrt(p*(1-p)/r['R'])) < 1e-12 for k, p in r['coverage'].items()), \"MC SE = sqrt(p(1-p)/R)\")\nok(all(abs(v*r['R'] - round(v*r['R'])) < 1e-9 for v in r['coverage'].values()), \"coverage*R integer\")\nf = sorted(k for k, v in r['coverage'].items() if v < 0.9)\nok(f == sorted(r['failure_cells']) and r['n_failure_cells'] == len(f), \"failure_cells consistent\")\nnm = sorted(k for k, v in r['coverage'].items() if abs(v-0.95) <= 2*r['mc_se_coverage'][k])\nok(nm == sorted(r['nominal_cells']), \"nominal_cells consistent\")\nok(r['n_analysed'] == r['n_cells']*r['R'] == 18000 and r['R']==2000 and r['B']==399, \"n_analysed=9*2000, R,B unchanged\")\nok(r['random_seed'] == int(d.seed[d.cell_id=='C1'].iloc[0]), \"random_seed = C1 seed\")\nex = pd.read_csv('out/exclusions.csv'); ok(18000 - ex.n_removed.sum() == r['n_analysed'], \"exclusion ledger sums\")\nfor est in ['cr0','cr1']:\n    for k in r['rel_bias_var']:\n        if k.startswith(est): ok(abs(r['rel_bias_var'][k.replace(est,'cr1' if est=='cr0' else 'cr0',1)] * 0 + 1) and True, \"\") if False else None\n# CR1/CR0 ratio must be G/(G-1)\nok(all(abs(r['rel_bias_var']['cr1'+k[3:]]/r['rel_bias_var'][k] - (int(k.split('_')[1][1:])/(int(k.split('_')[1][1:])-1))) < 1e-9\n       for k in r['rel_bias_var'] if k.startswith('cr0')), \"rel_bias cr1/cr0 = G/(G-1)\")\n# analytic E[V_CR0]/Var(ybar) vs MC estimate\nprint(\"Analytic vs MC relative bias of CR0 (z = (MC-exact)/MCSE):\")\nfor _, row in d.iterrows():\n    n = np.array([int(x) for x in row.cluster_sizes.split('|')], float); N = n.sum(); icc = row.icc\n    V = (icc*np.sum(n**2) + (1-icc)*N)/N**2\n    VT = icc*n**2 + (1-icc)*n\n    EV0 = np.sum(VT*(1-2*n/N) + n**2*V)/N**2\n    k = f\"cr0_G{row.G}_icc{icc:.1f}\"\n    z = (r['rel_bias_var'][k] - EV0/V)/r['mc_se_rel_bias_var'][k]\n    ok(abs(z) < 3, f\"  {k}: exact {EV0/V:.4f}  MC {r['rel_bias_var'][k]:.4f}  z={z:.2f}\")\n    dg = r['cell_diagnostics'][row.cell_id]\n    ok(abs(dg['var_exact'] - V) < 1e-15, f\"  {row.cell_id} var_exact matches formula\")\n# monotonicity sanity: cr1 halfwidth > cr0 halfwidth\nw = t.pivot(index='cell_id', columns='estimator', values='mean_ci_halfwidth')\nok((w.cr1 > w.cr0).all(), \"CR1 intervals wider than CR0 in every cell\")\nEOF\n~/wp29/venv/bin/python out/state/verify_checks.py","description":"Run verification checks incl. analytic bias"},"caller":{"type":"direct"}}],"container":null,"stop_reason":"tool_use","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":3779,"cache_read_input_tokens":18111,"output_tokens":2435,"output_tokens_details":{"thinking_tokens":644},"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":3779},"inference_geo":"global","iterations":[],"speed":"standard","fallback_credit":null},"input_transformations":[],"diagnostics":null,"context_management":null},"apiBlockIndex":1,"requestId":"req_011CgXt5aKDxRuJLJTnb5tAB","requestRef":"82778d22-022e-488d-9fdd-3b47e36f24d3","requestedModel":"claude-opus-5-5","type":"assistant","uuid":"26a26535-d099-4e53-968d-71e4973f5033","timestamp":"2026-10-30T05:37:52.995Z","effort":"medium","perTurnEffort":"medium","userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RT0019","sessionId":"47ce3250-2468-470f-8e8f-38dfc848301c","version":"2.1.314","gitBranch":"HEAD"}56{"parentUuid":"26a26535-d099-4e53-968d-71e4973f5033","isSidechain":false,"promptId":"08889d0b-f553-4d4f-849c-8211aa68e575","type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_01JABtE5Zx4xyJrAw6UTu2Lu","type":"tool_result","content":"PASS table1 has 27 rows, 3 estimators\nPASS key counts 27/27/18\nPASS rel_bias_var empty for wcb\nPASS coverages in [0,1]\nPASS MC SEs positive\nPASS MC SE = sqrt(p(1-p)/R)\nPASS coverage*R integer\nPASS failure_cells consistent\nPASS nominal_cells consistent\nPASS n_analysed=9*2000, R,B unchanged\nPASS random_seed = C1 seed\nPASS exclusion ledger sums\nPASS rel_bias cr1/cr0 = G/(G-1)\nAnalytic vs MC relative bias of CR0 (z = (MC-exact)/MCSE):\nPASS   cr0_G5_icc0.0: exact 0.6641  MC 0.6616  z=-0.19\nPASS   C1 var_exact matches formula\nPASS   cr0_G5_icc0.2: exact 0.4995  MC 0.5314  z=2.46\nPASS   C2 var_exact matches formula\nPASS   cr0_G5_icc0.5: exact 0.4883  MC 0.4834  z=-0.42\nPASS   C3 var_exact matches formula\nPASS   cr0_G10_icc0.0: exact 0.8393  MC 0.8558  z=1.40\nPASS   C4 var_exact matches formula\nPASS   cr0_G10_icc0.2: exact 0.7599  MC 0.7624  z=0.20\nPASS   C5 var_exact matches formula\nPASS   cr0_G10_icc0.5: exact 0.7544  MC 0.7737  z=1.37\nPASS   C6 var_exact matches formula\nPASS   cr0_G30_icc0.0: exact 0.9486  MC 0.9438  z=-0.70\nPASS   C7 var_exact matches formula\nPASS   cr0_G30_icc0.2: exact 0.9267  MC 0.9314  z=0.54\nPASS   C8 var_exact matches formula\nPASS   cr0_G30_icc0.5: exact 0.9253  MC 0.9253  z=-0.00\nPASS   C9 var_exact matches formula\nPASS CR1 intervals wider than CR0 in every cell","is_error":false}]},"uuid":"7245ae70-4d03-4614-9436-2adde5b809b7","timestamp":"2026-10-30T05:37:53.219Z","toolUseResult":{"stdout":"PASS table1 has 27 rows, 3 estimators\nPASS key counts 27/27/18\nPASS rel_bias_var empty for wcb\nPASS coverages in [0,1]\nPASS MC SEs positive\nPASS MC SE = sqrt(p(1-p)/R)\nPASS coverage*R integer\nPASS failure_cells consistent\nPASS nominal_cells consistent\nPASS n_analysed=9*2000, R,B unchanged\nPASS random_seed = C1 seed\nPASS exclusion ledger sums\nPASS rel_bias cr1/cr0 = G/(G-1)\nAnalytic vs MC relative bias of CR0 (z = (MC-exact)/MCSE):\nPASS   cr0_G5_icc0.0: exact 0.6641  MC 0.6616  z=-0.19\nPASS   C1 var_exact matches formula\nPASS   cr0_G5_icc0.2: exact 0.4995  MC 0.5314  z=2.46\nPASS   C2 var_exact matches formula\nPASS   cr0_G5_icc0.5: exact 0.4883  MC 0.4834  z=-0.42\nPASS   C3 var_exact matches formula\nPASS   cr0_G10_icc0.0: exact 0.8393  MC 0.8558  z=1.40\nPASS   C4 var_exact matches formula\nPASS   cr0_G10_icc0.2: exact 0.7599  MC 0.7624  z=0.20\nPASS   C5 var_exact matches formula\nPASS   cr0_G10_icc0.5: exact 0.7544  MC 0.7737  z=1.37\nPASS   C6 var_exact matches formula\nPASS   cr0_G30_icc0.0: exact 0.9486  MC 0.9438  z=-0.70\nPASS   C7 var_exact matches formula\nPASS   cr0_G30_icc0.2: exact 0.9267  MC 0.9314  z=0.54\nPASS   C8 var_exact matches formula\nPASS   cr0_G30_icc0.5: exact 0.9253  MC 0.9253  z=-0.00\nPASS   C9 var_exact matches formula\nPASS CR1 intervals wider than CR0 in every 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> out/state/verify_checks.py <<'EOF'\n# Verification checks for P4 (reads out/results.json, out/table1.csv, data/design.csv)\nimport json, numpy as np, pandas as pd\nr = json.load(open('out/results.json')); t = pd.read_csv('out/table1.csv'); d = pd.read_csv('data/design.csv')\nok = lambda c, m: print((\"PASS \" if c else \"FAIL \") + m)\nok(len(t) == 27 and set(t.estimator) == {'cr0','cr1','wcb'}, \"table1 has 27 rows, 3 estimators\")\nok(len(r['coverage']) == 27 and len(r['mc_se_coverage']) == 27 and len(r['rel_bias_var']) == 18, \"key counts 27/27/18\")\nok(t[t.estimator=='wcb'].rel_bias_var.isna().all(), \"rel_bias_var empty for wcb\")\nok(all(0 <= v <= 1 for v in r['coverage'].values()), \"coverages in [0,1]\")\nok(all(v > 0 for v in r['mc_se_coverage'].values()), \"MC SEs positive\")\nok(all(abs(r['mc_se_coverage'][k] - np.sqrt(p*(1-p)/r['R'])) < 1e-12 for k, p in r['coverage'].items()), \"MC SE = sqrt(p(1-p)/R)\")\nok(all(abs(v*r['R'] - round(v*r['R'])) < 1e-9 for v in r['coverage'].values()), \"coverage*R integer\")\nf = sorted(k for k, v in r['coverage'].items() if v < 0.9)\nok(f == sorted(r['failure_cells']) and r['n_failure_cells'] == len(f), \"failure_cells consistent\")\nnm = sorted(k for k, v in r['coverage'].items() if abs(v-0.95) <= 2*r['mc_se_coverage'][k])\nok(nm == sorted(r['nominal_cells']), \"nominal_cells consistent\")\nok(r['n_analysed'] == r['n_cells']*r['R'] == 18000 and r['R']==2000 and r['B']==399, \"n_analysed=9*2000, R,B unchanged\")\nok(r['random_seed'] == int(d.seed[d.cell_id=='C1'].iloc[0]), \"random_seed = C1 seed\")\nex = pd.read_csv('out/exclusions.csv'); ok(18000 - ex.n_removed.sum() == r['n_analysed'], \"exclusion ledger sums\")\nfor est in ['cr0','cr1']:\n    for k in r['rel_bias_var']:\n        if k.startswith(est): ok(abs(r['rel_bias_var'][k.replace(est,'cr1' if est=='cr0' else 'cr0',1)] * 0 + 1) and True, \"\") if False else None\n# CR1/CR0 ratio must be G/(G-1)\nok(all(abs(r['rel_bias_var']['cr1'+k[3:]]/r['rel_bias_var'][k] - (int(k.split('_')[1][1:])/(int(k.split('_')[1][1:])-1))) < 1e-9\n       for k in r['rel_bias_var'] if k.startswith('cr0')), \"rel_bias cr1/cr0 = G/(G-1)\")\n# analytic E[V_CR0]/Var(ybar) vs MC estimate\nprint(\"Analytic vs MC relative bias of CR0 (z = (MC-exact)/MCSE):\")\nfor _, row in d.iterrows():\n    n = np.array([int(x) for x in row.cluster_sizes.split('|')], float); N = n.sum(); icc = row.icc\n    V = (icc*np.sum(n**2) + (1-icc)*N)/N**2\n    VT = icc*n**2 + (1-icc)*n\n    EV0 = np.sum(VT*(1-2*n/N) + n**2*V)/N**2\n    k = f\"cr0_G{row.G}_icc{icc:.1f}\"\n    z = (r['rel_bias_var'][k] - EV0/V)/r['mc_se_rel_bias_var'][k]\n    ok(abs(z) < 3, f\"  {k}: exact {EV0/V:.4f}  MC {r['rel_bias_var'][k]:.4f}  z={z:.2f}\")\n    dg = r['cell_diagnostics'][row.cell_id]\n    ok(abs(dg['var_exact'] - V) < 1e-15, f\"  {row.cell_id} var_exact matches formula\")\n# monotonicity sanity: cr1 halfwidth > cr0 halfwidth\nw = t.pivot(index='cell_id', columns='estimator', values='mean_ci_halfwidth')\nok((w.cr1 > w.cr0).all(), \"CR1 intervals wider than CR0 in every cell\")\nEOF\n~/wp29/venv/bin/python out/state/verify_checks.py","description":"Run verification checks incl. analytic bias"}}]}}58{"type":"api-request","sessionId":"47ce3250-2468-470f-8e8f-38dfc848301c","id":"6192acdf-02f5-4b04-86e3-6cd69183ed6b","timestamp":"2026-10-30T05:37:53.221Z","version":"2.1.314","querySource":"sdk","shapeHash":"73d3b570cb757bc7e5d014e30ddf95327c833e905d81c3e64645bb812b4dbf54","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":"82778d22-022e-488d-9fdd-3b47e36f24d3","keep":18,"tail":["33eb57a517ef06cea4f65f9ed46a416de0fafab59d077196a11989b25a192525","76df9775d3cf7bccdf0cebbbb0117ab9b5990c5fe04cb17ca434946614bca945"],"cacheControl":[{"path":["system",0],"value":{"type":"ephemeral"}},{"path":["system",1],"value":{"type":"ephemeral"}},{"path":["messages",19,"content",0],"value":{"type":"ephemeral"}}],"requestDigest":"9cf95720f0be8e9b664f53eb0f5455cc407be9ef0a96b966207446b5536ccc34"}59{"type":"last-prompt","lastPrompt":"Begin","leafUuid":"7245ae70-4d03-4614-9436-2adde5b809b7","sessionId":"47ce3250-2468-470f-8e8f-38dfc848301c"}60{"type":"atis-latch","atis":"","sessionId":"47ce3250-2468-470f-8e8f-38dfc848301c"}61{"parentUuid":"7245ae70-4d03-4614-9436-2adde5b809b7","isSidechain":false,"message":{"model":"claude-opus-5-5","id":"msg_011CgXt78MqMwuHP77adrSw4","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":3201,"cache_read_input_tokens":21890,"output_tokens":993,"output_tokens_details":{"thinking_tokens":142},"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":3201},"inference_geo":"global","iterations":[],"speed":"standard","fallback_credit":null},"input_transformations":[],"diagnostics":null,"context_management":null},"thinkingDurationMs":1154,"thinkingDisplay":"updates","apiBlockIndex":0,"requestId":"req_011CgXt783iomkGKwExSMACk","requestRef":"6192acdf-02f5-4b04-86e3-6cd69183ed6b","requestedModel":"claude-opus-5-5","type":"assistant","uuid":"2a6e6f5a-c2a0-42f1-aaf9-bf8d3bf89980","timestamp":"2026-10-30T05:37:55.892Z","effort":"medium","perTurnEffort":"medium","userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RT0019","sessionId":"47ce3250-2468-470f-8e8f-38dfc848301c","version":"2.1.314","gitBranch":"HEAD"}62{"parentUuid":"2a6e6f5a-c2a0-42f1-aaf9-bf8d3bf89980","isSidechain":false,"message":{"model":"claude-opus-5-5","id":"msg_011CgXt78MqMwuHP77adrSw4","type":"message","role":"assistant","content":[{"type":"tool_use","id":"toolu_01K75oXkQUWeYnHyHxciH9zJ","name":"Bash","input":{"command":"~/wp29/venv/bin/python - <<'EOF'\np='out/state/verify_checks.py'; s=open(p).read()\ns=s.replace(\"\"\"for est in ['cr0','cr1']:\n    for k in r['rel_bias_var']:\n        if k.startswith(est): ok(abs(r['rel_bias_var'][k.replace(est,'cr1' if est=='cr0' else 'cr0',1)] * 0 + 1) and True, \"\") if False else None\n\"\"\",\"\")\nopen(p,'w').write(s)\np='out/analysis.py'; s=open(p).read()\ns=s.replace(\"\"\"    out[\"meta\"] = dict(var_exact=var_exact,\"\"\",\"\"\"    # analytic E[V_CR0] (for validation): Var(T_g - n_g*ybar) summed over g, / N^2\n    VT = icc * n.astype(float) ** 2 + (1 - icc) * n\n    ev0 = np.sum(VT * (1 - 2 * n / N) + n.astype(float) ** 2 * var_exact) / N ** 2\n    out[\"cr0\"][\"exact_rel_bias\"] = float(ev0 / var_exact)\n    out[\"cr1\"][\"exact_rel_bias\"] = float(adj * ev0 / var_exact)\n    out[\"meta\"] = dict(var_exact=var_exact,\"\"\")\ns=s.replace(\"\"\"            rb[k], rb_se[k] = o[est][\"rel_bias\"], o[est][\"rel_bias_se\"]\"\"\",\"\"\"            rb[k], rb_se[k] = o[est][\"rel_bias\"], o[est][\"rel_bias_se\"]\n            rb_exact[k] = o[est][\"exact_rel_bias\"]\"\"\")\ns=s.replace(\"cov, mcse, rb, rb_se,\",\"cov, mcse, rb, rb_se, rb_exact = {}, {}, {}, {}, {}\\nbase_cov, base_rb, cellmeta = {}, {}, {}\\n_unused,\")\ns=s.replace(\"\"\"    \"mc_se_rel_bias_var\": rb_se,\"\"\",\"\"\"    \"mc_se_rel_bias_var\": rb_se,\n    \"exact_rel_bias_var_analytic\": rb_exact,\"\"\")\nopen(p,'w').write(s)\nEOF\ngrep -n \"_unused\" out/analysis.py","description":"Add analytic bias key; tidy verify 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numbers as the first run. Let me look at the 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out && cat > state/checks.md <<'EOF'\n# Verification checks (P4)\nScript: `out/state/verify_checks.py`; full output in `out/state/verify_output.txt` (32 PASS, 0 FAIL).\n\n1. Reproducibility: deleted results.json/table1.csv/figure1.png, re-ran analysis.py twice; results.json byte-identical (cmp), table1.csv and figure1.png identical md5. PASS. (Later re-run after a cosmetic figure-tick change: results.json unchanged.)\n2. table1.csv has 27 rows, 3 estimators; rel_bias_var empty for wcb. PASS\n3. coverage 27 keys, mc_se 27, rel_bias_var 18. PASS\n4. coverages in [0,1]; coverage×R integer; MC SE > 0 and equals sqrt(p(1−p)/R). PASS\n5. failure_cells/n_failure_cells and nominal_cells recomputed independently from coverage & MC SE: match. PASS\n6. R=2000, B=399, n_cells=9, n_analysed=18000 = 9×2000; random_seed = C1 seed 20261101. PASS\n7. Exclusion ledger: 18000 − 0 = 18000 = n_analysed. PASS\n8. rel_bias(CR1)/rel_bias(CR0) = G/(G−1) exactly in every cell. PASS\n9. Exact Var(ȳ) in each cell matches formula; empirical Var(ȳ) over R replications close to it (cell_diagnostics). PASS\n10. Analytic E[V̂_CR0]/Var(ȳ) (derived: Σ_g Var(T_g − n_g ȳ)/N²) vs MC estimate: |z| < 3 in all 9 cells; largest |z| = 2.46 (C2, G=5 ICC=0.2), others ≤ 1.40. With 9 comparisons one |z|≈2.5 is not unusual; PASS with note.\n11. CR1 mean half-width > CR0 in every cell (as must hold since t_{G−1}·sqrt(G/(G−1)) > 1.96). PASS\n12. Baseline (naive iid interval) behaves as theory predicts: rel. var bias ≈1.00 at ICC=0, ≈0.10 at ICC=0.2, ≈0.04 at ICC=0.5; coverage ≈0.95 at ICC=0 and 0.29–0.49 otherwise. PASS (magnitude sanity).\n13. Runtime ≈ 2 s ≪ 600 s limit. PASS\nEOF\n~/wp29/venv/bin/python - <<'EOF'\nimport json\np=json.load(open('state/phases.json'))\np['P1']={\"status\":\"passed\",\"checks\":[\"SHA-256 computed and matched bundle.json\",\"schema/missing/duplicates checked\",\"len(cluster_sizes)=G and sum=N for all 9 rows\",\"exclusions.csv written (0 removed)\"],\"notes\":\"G=5 Rademacher discreteness noted\"}\np['P2']={\"status\":\"passed\",\"checks\":[\"naive iid-SE interval coverage and var bias computed under same evaluation rule (baseline_iid_* keys)\"],\"notes\":\"baseline badly undercovers when ICC>0\"}\np['P3']={\"status\":\"passed\",\"checks\":[\"analysis.py reads only data/design.csv\",\"per-cell seeds from design.csv\",\"bootstrap vectorised B x G\",\"writes results.json, table1.csv, figure1.png\"],\"notes\":\"runtime ~2s\"}\np['P4']={\"status\":\"passed\",\"checks\":[\"clean re-run: results.json byte-identical, table/figure md5 identical\",\"32/32 internal consistency checks PASS (state/verify_output.txt)\",\"analytic CR0 bias agrees with MC (max |z|=2.46)\"],\"notes\":\"see state/checks.md\"}\np['P5']={\"status\":\"running\",\"checks\":[],\"notes\":\"\"}\njson.dump(p,open('state/phases.json','w'),indent=1)\nEOF\ncat >> state/log.md <<'EOF'\n- 2026-10-30 P2 passed: baseline = naive iid interval (baseline_iid_* keys).\n- 2026-10-30 P3 passed: analysis.py implemented; quantile = numpy linear default (decision documented).\n- 2026-10-30 Decision: added supplementary keys (exact_rel_bias_var_analytic, mc_se_rel_bias_var, cell_diagnostics) for validation.\n- 2026-10-30 P4 passed: determinism + 32 checks. 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Reproducibility: deleted results.json/table1.csv/figure1.png, re-ran analysis.py twice; results.json byte-identical (cmp), table1.csv and figure1.png identical md5. PASS. (Later re-run after a cosmetic figure-tick change: results.json unchanged.)\n2. table1.csv has 27 rows, 3 estimators; rel_bias_var empty for wcb. PASS\n3. coverage 27 keys, mc_se 27, rel_bias_var 18. PASS\n4. coverages in [0,1]; coverage×R integer; MC SE > 0 and equals sqrt(p(1−p)/R). PASS\n5. failure_cells/n_failure_cells and nominal_cells recomputed independently from coverage & MC SE: match. PASS\n6. R=2000, B=399, n_cells=9, n_analysed=18000 = 9×2000; random_seed = C1 seed 20261101. PASS\n7. Exclusion ledger: 18000 − 0 = 18000 = n_analysed. PASS\n8. rel_bias(CR1)/rel_bias(CR0) = G/(G−1) exactly in every cell. PASS\n9. Exact Var(ȳ) in each cell matches formula; empirical Var(ȳ) over R replications close to it (cell_diagnostics). PASS\n10. 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PASS\nEOF\n~/wp29/venv/bin/python - <<'EOF'\nimport json\np=json.load(open('state/phases.json'))\np['P1']={\"status\":\"passed\",\"checks\":[\"SHA-256 computed and matched bundle.json\",\"schema/missing/duplicates checked\",\"len(cluster_sizes)=G and sum=N for all 9 rows\",\"exclusions.csv written (0 removed)\"],\"notes\":\"G=5 Rademacher discreteness noted\"}\np['P2']={\"status\":\"passed\",\"checks\":[\"naive iid-SE interval coverage and var bias computed under same evaluation rule (baseline_iid_* keys)\"],\"notes\":\"baseline badly undercovers when ICC>0\"}\np['P3']={\"status\":\"passed\",\"checks\":[\"analysis.py reads only data/design.csv\",\"per-cell seeds from design.csv\",\"bootstrap vectorised B x G\",\"writes results.json, table1.csv, figure1.png\"],\"notes\":\"runtime ~2s\"}\np['P4']={\"status\":\"passed\",\"checks\":[\"clean re-run: results.json byte-identical, table/figure md5 identical\",\"32/32 internal consistency checks PASS (state/verify_output.txt)\",\"analytic CR0 bias agrees with MC (max |z|=2.46)\"],\"notes\":\"see state/checks.md\"}\np['P5']={\"status\":\"running\",\"checks\":[],\"notes\":\"\"}\njson.dump(p,open('state/phases.json','w'),indent=1)\nEOF\ncat >> state/log.md <<'EOF'\n- 2026-10-30 P2 passed: baseline = naive iid interval (baseline_iid_* keys).\n- 2026-10-30 P3 passed: analysis.py implemented; quantile = numpy linear default (decision documented).\n- 2026-10-30 Decision: added supplementary keys (exact_rel_bias_var_analytic, mc_se_rel_bias_var, cell_diagnostics) for validation.\n- 2026-10-30 P4 passed: determinism + 32 checks. 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0.7805 0.0093 0.662 0.664\ncr1_G5_icc0.0 0.9115 0.0064 0.827 0.83\nwcb_G5_icc0.0 0.855 0.0079 nan nan\ncr0_G5_icc0.2 0.721 0.01 0.531 0.5\ncr1_G5_icc0.2 0.8845 0.0071 0.664 0.624\nwcb_G5_icc0.2 0.854 0.0079 nan nan\ncr0_G5_icc0.5 0.717 0.0101 0.483 0.488\ncr1_G5_icc0.5 0.8755 0.0074 0.604 0.61\nwcb_G5_icc0.5 0.849 0.008 nan nan\ncr0_G10_icc0.0 0.8965 0.0068 0.856 0.839\ncr1_G10_icc0.0 0.9435 0.0052 0.951 0.933\nwcb_G10_icc0.0 0.936 0.0055 nan nan\ncr0_G10_icc0.2 0.857 0.0078 0.762 0.76\ncr1_G10_icc0.2 0.9155 0.0062 0.847 0.844\nwcb_G10_icc0.2 0.9295 0.0057 nan nan\ncr0_G10_icc0.5 0.853 0.0079 0.774 0.754\ncr1_G10_icc0.5 0.9065 0.0065 0.86 0.838\nwcb_G10_icc0.5 0.9325 0.0056 nan nan\ncr0_G30_icc0.0 0.9275 0.0058 0.944 0.949\ncr1_G30_icc0.0 0.94 0.0053 0.976 0.981\nwcb_G30_icc0.0 0.943 0.0052 nan nan\ncr0_G30_icc0.2 0.921 0.006 0.931 0.927\ncr1_G30_icc0.2 0.935 0.0055 0.963 0.959\nwcb_G30_icc0.2 0.946 0.0051 nan nan\ncr0_G30_icc0.5 0.9165 0.0062 0.925 0.925\ncr1_G30_icc0.5 0.93 0.0057 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round(r['rel_bias_var'].get(k,float('nan')),3), round(r['exact_rel_bias_var_analytic'].get(k,float('nan')),3))\nprint(r['avg_coverage_small_G'])\"","description":"Print results for report 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> out/report.md <<'EOF'\n# Coverage of three cluster-robust 95% confidence intervals for a mean when there are few clusters: a simulation study (T2-simulation)\n\nReproduce with `~/wp29/venv/bin/python out/analysis.py`, run from the workspace root. The script reads only `data/design.csv` and writes `out/results.json`, `out/table1.csv` and `out/figure1.png`. It takes about 2 seconds and gives the same output every time it runs. Every number below is quoted from `out/results.json`, and the key it comes from is shown in brackets. A key such as `coverage[cr1_G5_icc0.2]` means the entry `cr1_G5_icc0.2` of the object `coverage`.\n\n## Question\nObservations come in G clusters. We estimate the population mean μ of y by the sample mean ȳ. How close to 95% is the actual coverage of three intervals?\n- **CR0**: the cluster sandwich variance with a normal critical value of 1.96.\n- **CR1**: CR0 multiplied by G/(G−1), with a Student t critical value on G−1 degrees of freedom.\n- **WCB**: the unrestricted wild cluster bootstrap-t with Rademacher weights and B = 399 draws.\n\nThe design has 9 cells: G ∈ {5, 10, 30} crossed with ICC ∈ {0, 0.2, 0.5}. For each procedure and cell we estimate:\n- the coverage;\n- its Monte Carlo (MC) standard error;\n- for CR0 and CR1, the relative bias of the variance estimator, E[V̂]/Var(ȳ), using the exact Var(ȳ);\n- whether the pair is a failure regime (coverage < 0.90).\n\n## Data and exclusions\n- **Input.** There is no observed data. The only input is the design file `data/design.csv`, with 9 rows (`n_input_rows`). Its SHA-256 matches the bundle manifest. The audit is in `out/state/data_audit.md`.\n- **Design checks.** Cluster sizes follow 8, 16, 32, 64 repeated. In every row the number of sizes equals G and their sum equals N.\n- **Settings used.** R = 2000 replications per cell (`R`) and B = 399 bootstrap draws (`B`), for 9 cells (`n_cells`) and 18,000 simulated samples in total (`n_analysed`). Seeds are per cell, from `design.csv`; cell C1 uses 20261101 (`random_seed`).\n- **Exclusions.** None. `out/exclusions.csv` records 0 removed, so 18,000 − 0 = 18,000.\n- **Design issues found** (`data_issues`):\n  1. When ICC = 0, u_g ~ N(0, 0) has zero variance. I draw u_g = √ICC·Z, which is exactly 0.\n  2. With G = 5, Rademacher weights allow only 2⁵ = 32 weight vectors. Because flipping every sign leaves |t*| unchanged, there are at most 16 distinct |t*| values, so the bootstrap critical value can take only a few values.\n  3. \"The 0.95 empirical quantile\" is not uniquely defined for finite B. I used numpy's default linear-interpolation quantile.\n\n## Method\n**Data-generating process.** In each replication, y_ig = μ + u_g + e_ig, with μ = 0, u_g ~ N(0, ICC) and e_ig ~ N(0, 1−ICC). The script creates one `numpy.random.default_rng(seed)` per cell. Within each replication it draws, in this order: G normals for u, then N normals for e, then a B×G matrix of Rademacher weights.\n\n**Shared quantities.** The residuals are ê = y − ȳ, and S_g is the sum of the residuals in cluster g.\n- V̂_CR0 = ΣS_g²/N².\n- V̂_CR1 = G/(G−1)·V̂_CR0.\n\n**The three intervals.**\n- CR0: ȳ ± 1.96·√V̂_CR0.\n- CR1: ȳ ± t_{0.975,G−1}·√V̂_CR1.\n- WCB: draw y* = ȳ + w_g·ê. Then ȳ* − ȳ = Σ w_g S_g / N, and the cluster sums of the bootstrap residuals are w_g S_g − n_g(ȳ* − ȳ). From these, compute V̂*_CR1 and t*_b = (ȳ* − ȳ)/√V̂*_CR1. This is algebraically identical to recomputing on the full bootstrap sample, and it is vectorised as one B×G matrix product. The critical value q is the 0.95 quantile of |t*|, and the interval is ȳ ± q·√V̂_CR1.\n\n**Outputs computed.**\n- Coverage is the fraction of the R intervals that contain μ. Its MC SE is √(p̂(1−p̂)/R).\n- The relative bias is mean(V̂) divided by the exact Var(ȳ) = [ICC·Σn_g² + (1−ICC)N]/N².\n- A pair is a failure if p̂ < 0.90. It is consistent with nominal coverage if |p̂ − 0.95| ≤ 2·MC SE.\n\n**Baseline (phase P2).** A naive interval that ignores clustering: ȳ ± 1.96·s/√N (`baseline_iid_coverage`, `baseline_iid_rel_bias_var`).\n\n**Validation.** I derived E[V̂_CR0] analytically as Σ_g Var(T_g − n_g ȳ)/N², where T_g is the cluster total (`exact_rel_bias_var_analytic`), and compared it with the MC estimate. The checks are listed in `out/state/checks.md`.\n\n## Results\nFull results are in `out/table1.csv` and `out/figure1.png`. Coverage, with the MC SE in parentheses (`coverage`, `mc_se_coverage`):\n\n| cell | CR0 | CR1 | WCB |\n|---|---|---|---|\n| G=5, ICC=0.0 | 0.7805 (0.0093) | 0.9115 (0.0064) | 0.8550 (0.0079) |\n| G=5, ICC=0.2 | 0.7210 (0.0100) | 0.8845 (0.0071) | 0.8540 (0.0079) |\n| G=5, ICC=0.5 | 0.7170 (0.0101) | 0.8755 (0.0074) | 0.8490 (0.0080) |\n| G=10, ICC=0.0 | 0.8965 (0.0068) | 0.9435 (0.0052) | 0.9360 (0.0055) |\n| G=10, ICC=0.2 | 0.8570 (0.0078) | 0.9155 (0.0062) | 0.9295 (0.0057) |\n| G=10, ICC=0.5 | 0.8530 (0.0079) | 0.9065 (0.0065) | 0.9325 (0.0056) |\n| G=30, ICC=0.0 | 0.9275 (0.0058) | 0.9400 (0.0053) | 0.9430 (0.0052) |\n| G=30, ICC=0.2 | 0.9210 (0.0060) | 0.9350 (0.0055) | 0.9460 (0.0051) |\n| G=30, ICC=0.5 | 0.9165 (0.0062) | 0.9300 (0.0057) | 0.9425 (0.0052) |\n\nRelative bias of the variance estimator, E[V̂]/Var(ȳ) (`rel_bias_var`):\n\n| cell | CR0 | CR1 |\n|---|---|---|\n| G=5, ICC=0.0 / 0.2 / 0.5 | 0.662 / 0.531 / 0.483 | 0.827 / 0.664 / 0.604 |\n| G=10, ICC=0.0 / 0.2 / 0.5 | 0.856 / 0.762 / 0.774 | 0.951 / 0.847 / 0.860 |\n| G=30, ICC=0.0 / 0.2 / 0.5 | 0.944 / 0.931 / 0.925 | 0.976 / 0.963 / 0.957 |\n\n**Main findings.**\n1. **Failure regimes.** 11 of the 27 pairs have coverage below 0.90 (`n_failure_cells`, `failure_cells`):\n   - CR0 in all six cells with G ≤ 10;\n   - CR1 at G = 5 with ICC = 0.2 and ICC = 0.5;\n   - WCB in all three G = 5 cells.\n   No procedure fails at G = 30.\n2. **CR0 understates the variance substantially when there are few clusters.** At G = 5 its E[V̂]/Var(ȳ) is between 0.48 and 0.66 (`rel_bias_var[cr0_G5_icc*]`). Its coverage is 0.72–0.78 at G = 5 and 0.85–0.90 at G = 10 (`coverage[cr0_*]`).\n3. **CR1 is the best of the three at G = 5, but still below nominal there.** Its average coverage over the G = 5 cells is 0.8905, against 0.8527 for WCB and 0.7395 for CR0 (`avg_coverage_small_G`), so `best_procedure_small_G` = \"cr1\". Even after the G/(G−1) correction, CR1's variance estimate is still biased downward (0.60–0.83 at G = 5).\n4. **WCB is the best of the three at G = 10 with ICC > 0, and at G = 30.** Its coverage is 0.9295 and 0.9325 at G = 10 with ICC 0.2 and 0.5, and 0.943–0.946 at G = 30 (`coverage[wcb_*]`).\n5. **Only five pairs are consistent with nominal coverage** (`nominal_cells`): CR1 at G = 10 and G = 30 with ICC = 0, and WCB in all three G = 30 cells.\n6. **Bias worsens as ICC rises from 0 to 0.2 at small G.** At G = 5 and 10, all three procedures lose coverage when ICC goes from 0 to 0.2, and change little between 0.2 and 0.5. The likely reason is that unequal cluster sizes (8 to 64) let the large clusters dominate Var(ȳ) once there is cluster-level variance.\n7. **The baseline shows why a clustered interval is needed.** The naive interval has about 0.94–0.96 coverage at ICC = 0, but only 0.29–0.49 when ICC > 0 (`baseline_iid_coverage`).\n\n## Uncertainty\n- **Coverage.** Each coverage estimate is a binomial proportion from R = 2000 replications. Its MC SE is 0.005–0.010 (`mc_se_coverage`); the largest is 0.0101, for CR0 at G = 5 and ICC = 0.5. A 95% MC interval is therefore about ±0.010–0.020 around each estimate.\n- **Classification of failures.** Most failure classifications are far from the borderline. The exception is `cr0_G10_icc0.0` (0.8965, SE 0.0068), which is only 0.5 SE below 0.90, so whether it counts as a failure is uncertain.\n- **CR1 at G = 5.** `cr1_G5_icc0.0` (0.9115) is above 0.90 by 1.8 SE, but it is still well below 0.95.\n- **Relative bias.** The MC SE of the relative-bias estimates is 0.007–0.016 (`mc_se_rel_bias_var`). The MC estimates agree with the analytic E[V̂_CR0]/Var(ȳ) (`exact_rel_bias_var_analytic`) within 2.5 MC SE in all 9 cells. The largest deviation, z = 2.46, is in the G = 5, ICC = 0.2 cell; with 9 comparisons, one deviation of this size is not unusual. When citing CR0/CR1 bias, the analytic values should be preferred, because they have no MC error.\n- **Bootstrap.** Using B = 399 adds bootstrap noise to each replication's critical value. Its effect is already included in the WCB coverage estimate. It is not reported separately.\n- **Multiple comparisons.** The 27 \"consistent with nominal\" checks are not adjusted for multiplicity, so roughly one pair could be misclassified by chance.\n\n## Limitations and open issues\n- **Distributions.** Only Gaussian, homoskedastic u and e were studied. Skewed or heavy-tailed errors, which are known to hurt few-cluster inference more, are not covered.\n- **Heterogeneity.** There is a single fixed pattern of cluster sizes (8/16/32/64) and the ICC is the same across clusters. The design does not vary the degree of size imbalance, and it has no cluster-specific variances or heterogeneous effects. Results may differ with balanced clusters or more extreme imbalance.\n- **Regressors.** Only an intercept (the mean) is estimated. Inference on regression slopes, leverage from regressors, treatment indicators that vary only at the cluster level, and few treated clusters are not studied.\n- **Other estimators and bootstrap variants.** The following are not evaluated:\n  - CR2/CR3 (bias-reduced linearisation) and Bell–McCaffrey or Satterthwaite degrees of freedom;\n  - the restricted (null-imposed) wild bootstrap and inverted tests;\n  - Webb six-point or Mammen weights;\n  - full enumeration of the 2^G weight vectors.\n- **Why WCB does poorly at G = 5 is not settled.** With Rademacher weights, the G = 5 bootstrap distribution has at most 16 distinct |t*| values. WCB's poor coverage there is at least partly caused by this discreteness, and Webb weights would probably behave differently. That is not tested here.\n- **Choice of quantile rule.** The WCB quantile uses numpy's linear interpolation. Another quantile rule, such as the 380th order statistic of 399, could shift WCB coverage slightly, especially at G = 5. This sensitivity was not run, to stay within the specified design.\n- **Scope of the grid.** Only G ∈ {5, 10, 30} and ICC ∈ {0, 0.2, 0.5} were run. Between grid points the behaviour is interpolation, not measurement.\nEOF\ncat > out/claims.json <<'EOF'\n[\n {\"id\":\"C1\",\"text\":\"11 of 27 (procedure, cell) pairs are failure regimes (coverage < 0.90): CR0 in all G<=10 cells, CR1 at G=5 with ICC 0.2 and 0.5, and WCB in all G=5 cells; no procedure fails at G=30.\",\"results_keys\":[\"failure_cells\",\"n_failure_cells\",\"coverage\"],\"status\":\"supported\",\"caveat\":\"cr0_G10_icc0.0 (0.8965) is only ~0.5 MC SE below 0.90.\"},\n {\"id\":\"C2\",\"text\":\"CR0 substantially underestimates Var(ybar) with few clusters (E[V]/Var 0.48-0.66 at G=5) and its coverage is 0.72-0.78 at G=5.\",\"results_keys\":[\"rel_bias_var\",\"exact_rel_bias_var_analytic\",\"coverage\"],\"status\":\"supported\",\"caveat\":\"MC bias estimates agree with analytic values within 2.5 MC SE.\"},\n {\"id\":\"C3\",\"text\":\"CR1 has the highest average coverage over the G=5 cells (0.8905 vs 0.8527 WCB and 0.7395 CR0) but remains below nominal there.\",\"results_keys\":[\"best_procedure_small_G\",\"avg_coverage_small_G\",\"coverage\"],\"status\":\"supported\",\"caveat\":\"Specific to Rademacher WCB with linear-interpolation quantile; G=5 Rademacher bootstrap is highly discrete.\"},\n {\"id\":\"C4\",\"text\":\"WCB gives the coverage closest to nominal at G=10 with ICC>0 and at G=30, and is consistent with nominal in all three G=30 cells.\",\"results_keys\":[\"coverage\",\"nominal_cells\"],\"status\":\"supported\",\"caveat\":\"Differences between WCB and CR1 at G=30, ICC=0 are within MC error.\"},\n {\"id\":\"C5\",\"text\":\"Only five pairs are consistent with nominal coverage (|p-0.95| <= 2 MC SE): cr1 at G=10 and G=30 with ICC=0, and wcb in all G=30 cells.\",\"results_keys\":[\"nominal_cells\",\"mc_se_coverage\"],\"status\":\"supported\",\"caveat\":\"No multiplicity adjustment across 27 comparisons.\"},\n {\"id\":\"C6\",\"text\":\"Monte Carlo SEs of coverage are 0.005-0.010 with R=2000.\",\"results_keys\":[\"mc_se_coverage\",\"R\"],\"status\":\"supported\",\"caveat\":\"\"},\n {\"id\":\"C7\",\"text\":\"The naive iid interval (baseline) covers ~0.95 at ICC=0 but only 0.29-0.49 when ICC>0.\",\"results_keys\":[\"baseline_iid_coverage\",\"baseline_iid_rel_bias_var\"],\"status\":\"supported\",\"caveat\":\"Supplementary baseline, not a required estimand.\"}\n]\nEOF\n~/wp29/venv/bin/python - <<'EOF'\nimport json\nr=json.load(open('out/results.json')); c=json.load(open('out/claims.json'))\nprint(all(k in r for x in c for k in x['results_keys']))\np=json.load(open('out/state/phases.json'))\np['P5']={\"status\":\"passed\",\"checks\":[\"report.md has 6 required sections\",\"claims.json keys all exist in results.json\",\"all report numbers taken from results.json\"],\"notes\":\"\"}\njson.dump(p,open('out/state/phases.json','w'),indent=1)\ns=open('out/report.md').read()\nprint([h for h in [\"## Question\",\"## Data and exclusions\",\"## Method\",\"## Results\",\"## Uncertainty\",\"## Limitations and open issues\"] if h not in s])\nEOF\necho \"- 2026-10-30 P5 passed: report.md and claims.json written; all phases passed.\" >> out/state/log.md; ls out out/state","description":"Write report, claims, finalize 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> out/report.md <<'EOF'\n# Coverage of three cluster-robust 95% confidence intervals for a mean when there are few clusters: a simulation study (T2-simulation)\n\nReproduce with `~/wp29/venv/bin/python out/analysis.py`, run from the workspace root. The script reads only `data/design.csv` and writes `out/results.json`, `out/table1.csv` and `out/figure1.png`. It takes about 2 seconds and gives the same output every time it runs. Every number below is quoted from `out/results.json`, and the key it comes from is shown in brackets. A key such as `coverage[cr1_G5_icc0.2]` means the entry `cr1_G5_icc0.2` of the object `coverage`.\n\n## Question\nObservations come in G clusters. We estimate the population mean μ of y by the sample mean ȳ. How close to 95% is the actual coverage of three intervals?\n- **CR0**: the cluster sandwich variance with a normal critical value of 1.96.\n- **CR1**: CR0 multiplied by G/(G−1), with a Student t critical value on G−1 degrees of freedom.\n- **WCB**: the unrestricted wild cluster bootstrap-t with Rademacher weights and B = 399 draws.\n\nThe design has 9 cells: G ∈ {5, 10, 30} crossed with ICC ∈ {0, 0.2, 0.5}. For each procedure and cell we estimate:\n- the coverage;\n- its Monte Carlo (MC) standard error;\n- for CR0 and CR1, the relative bias of the variance estimator, E[V̂]/Var(ȳ), using the exact Var(ȳ);\n- whether the pair is a failure regime (coverage < 0.90).\n\n## Data and exclusions\n- **Input.** There is no observed data. The only input is the design file `data/design.csv`, with 9 rows (`n_input_rows`). Its SHA-256 matches the bundle manifest. The audit is in `out/state/data_audit.md`.\n- **Design checks.** Cluster sizes follow 8, 16, 32, 64 repeated. In every row the number of sizes equals G and their sum equals N.\n- **Settings used.** R = 2000 replications per cell (`R`) and B = 399 bootstrap draws (`B`), for 9 cells (`n_cells`) and 18,000 simulated samples in total (`n_analysed`). Seeds are per cell, from `design.csv`; cell C1 uses 20261101 (`random_seed`).\n- **Exclusions.** None. `out/exclusions.csv` records 0 removed, so 18,000 − 0 = 18,000.\n- **Design issues found** (`data_issues`):\n  1. When ICC = 0, u_g ~ N(0, 0) has zero variance. I draw u_g = √ICC·Z, which is exactly 0.\n  2. With G = 5, Rademacher weights allow only 2⁵ = 32 weight vectors. Because flipping every sign leaves |t*| unchanged, there are at most 16 distinct |t*| values, so the bootstrap critical value can take only a few values.\n  3. \"The 0.95 empirical quantile\" is not uniquely defined for finite B. I used numpy's default linear-interpolation quantile.\n\n## Method\n**Data-generating process.** In each replication, y_ig = μ + u_g + e_ig, with μ = 0, u_g ~ N(0, ICC) and e_ig ~ N(0, 1−ICC). The script creates one `numpy.random.default_rng(seed)` per cell. Within each replication it draws, in this order: G normals for u, then N normals for e, then a B×G matrix of Rademacher weights.\n\n**Shared quantities.** The residuals are ê = y − ȳ, and S_g is the sum of the residuals in cluster g.\n- V̂_CR0 = ΣS_g²/N².\n- V̂_CR1 = G/(G−1)·V̂_CR0.\n\n**The three intervals.**\n- CR0: ȳ ± 1.96·√V̂_CR0.\n- CR1: ȳ ± t_{0.975,G−1}·√V̂_CR1.\n- WCB: draw y* = ȳ + w_g·ê. Then ȳ* − ȳ = Σ w_g S_g / N, and the cluster sums of the bootstrap residuals are w_g S_g − n_g(ȳ* − ȳ). From these, compute V̂*_CR1 and t*_b = (ȳ* − ȳ)/√V̂*_CR1. This is algebraically identical to recomputing on the full bootstrap sample, and it is vectorised as one B×G matrix product. The critical value q is the 0.95 quantile of |t*|, and the interval is ȳ ± q·√V̂_CR1.\n\n**Outputs computed.**\n- Coverage is the fraction of the R intervals that contain μ. Its MC SE is √(p̂(1−p̂)/R).\n- The relative bias is mean(V̂) divided by the exact Var(ȳ) = [ICC·Σn_g² + (1−ICC)N]/N².\n- A pair is a failure if p̂ < 0.90. It is consistent with nominal coverage if |p̂ − 0.95| ≤ 2·MC SE.\n\n**Baseline (phase P2).** A naive interval that ignores clustering: ȳ ± 1.96·s/√N (`baseline_iid_coverage`, `baseline_iid_rel_bias_var`).\n\n**Validation.** I derived E[V̂_CR0] analytically as Σ_g Var(T_g − n_g ȳ)/N², where T_g is the cluster total (`exact_rel_bias_var_analytic`), and compared it with the MC estimate. The checks are listed in `out/state/checks.md`.\n\n## Results\nFull results are in `out/table1.csv` and `out/figure1.png`. Coverage, with the MC SE in parentheses (`coverage`, `mc_se_coverage`):\n\n| cell | CR0 | CR1 | WCB |\n|---|---|---|---|\n| G=5, ICC=0.0 | 0.7805 (0.0093) | 0.9115 (0.0064) | 0.8550 (0.0079) |\n| G=5, ICC=0.2 | 0.7210 (0.0100) | 0.8845 (0.0071) | 0.8540 (0.0079) |\n| G=5, ICC=0.5 | 0.7170 (0.0101) | 0.8755 (0.0074) | 0.8490 (0.0080) |\n| G=10, ICC=0.0 | 0.8965 (0.0068) | 0.9435 (0.0052) | 0.9360 (0.0055) |\n| G=10, ICC=0.2 | 0.8570 (0.0078) | 0.9155 (0.0062) | 0.9295 (0.0057) |\n| G=10, ICC=0.5 | 0.8530 (0.0079) | 0.9065 (0.0065) | 0.9325 (0.0056) |\n| G=30, ICC=0.0 | 0.9275 (0.0058) | 0.9400 (0.0053) | 0.9430 (0.0052) |\n| G=30, ICC=0.2 | 0.9210 (0.0060) | 0.9350 (0.0055) | 0.9460 (0.0051) |\n| G=30, ICC=0.5 | 0.9165 (0.0062) | 0.9300 (0.0057) | 0.9425 (0.0052) |\n\nRelative bias of the variance estimator, E[V̂]/Var(ȳ) (`rel_bias_var`):\n\n| cell | CR0 | CR1 |\n|---|---|---|\n| G=5, ICC=0.0 / 0.2 / 0.5 | 0.662 / 0.531 / 0.483 | 0.827 / 0.664 / 0.604 |\n| G=10, ICC=0.0 / 0.2 / 0.5 | 0.856 / 0.762 / 0.774 | 0.951 / 0.847 / 0.860 |\n| G=30, ICC=0.0 / 0.2 / 0.5 | 0.944 / 0.931 / 0.925 | 0.976 / 0.963 / 0.957 |\n\n**Main findings.**\n1. **Failure regimes.** 11 of the 27 pairs have coverage below 0.90 (`n_failure_cells`, `failure_cells`):\n   - CR0 in all six cells with G ≤ 10;\n   - CR1 at G = 5 with ICC = 0.2 and ICC = 0.5;\n   - WCB in all three G = 5 cells.\n   No procedure fails at G = 30.\n2. **CR0 understates the variance substantially when there are few clusters.** At G = 5 its E[V̂]/Var(ȳ) is between 0.48 and 0.66 (`rel_bias_var[cr0_G5_icc*]`). Its coverage is 0.72–0.78 at G = 5 and 0.85–0.90 at G = 10 (`coverage[cr0_*]`).\n3. **CR1 is the best of the three at G = 5, but still below nominal there.** Its average coverage over the G = 5 cells is 0.8905, against 0.8527 for WCB and 0.7395 for CR0 (`avg_coverage_small_G`), so `best_procedure_small_G` = \"cr1\". Even after the G/(G−1) correction, CR1's variance estimate is still biased downward (0.60–0.83 at G = 5).\n4. **WCB is the best of the three at G = 10 with ICC > 0, and at G = 30.** Its coverage is 0.9295 and 0.9325 at G = 10 with ICC 0.2 and 0.5, and 0.943–0.946 at G = 30 (`coverage[wcb_*]`).\n5. **Only five pairs are consistent with nominal coverage** (`nominal_cells`): CR1 at G = 10 and G = 30 with ICC = 0, and WCB in all three G = 30 cells.\n6. **Bias worsens as ICC rises from 0 to 0.2 at small G.** At G = 5 and 10, all three procedures lose coverage when ICC goes from 0 to 0.2, and change little between 0.2 and 0.5. The likely reason is that unequal cluster sizes (8 to 64) let the large clusters dominate Var(ȳ) once there is cluster-level variance.\n7. **The baseline shows why a clustered interval is needed.** The naive interval has about 0.94–0.96 coverage at ICC = 0, but only 0.29–0.49 when ICC > 0 (`baseline_iid_coverage`).\n\n## Uncertainty\n- **Coverage.** Each coverage estimate is a binomial proportion from R = 2000 replications. Its MC SE is 0.005–0.010 (`mc_se_coverage`); the largest is 0.0101, for CR0 at G = 5 and ICC = 0.5. A 95% MC interval is therefore about ±0.010–0.020 around each estimate.\n- **Classification of failures.** Most failure classifications are far from the borderline. The exception is `cr0_G10_icc0.0` (0.8965, SE 0.0068), which is only 0.5 SE below 0.90, so whether it counts as a failure is uncertain.\n- **CR1 at G = 5.** `cr1_G5_icc0.0` (0.9115) is above 0.90 by 1.8 SE, but it is still well below 0.95.\n- **Relative bias.** The MC SE of the relative-bias estimates is 0.007–0.016 (`mc_se_rel_bias_var`). The MC estimates agree with the analytic E[V̂_CR0]/Var(ȳ) (`exact_rel_bias_var_analytic`) within 2.5 MC SE in all 9 cells. The largest deviation, z = 2.46, is in the G = 5, ICC = 0.2 cell; with 9 comparisons, one deviation of this size is not unusual. When citing CR0/CR1 bias, the analytic values should be preferred, because they have no MC error.\n- **Bootstrap.** Using B = 399 adds bootstrap noise to each replication's critical value. Its effect is already included in the WCB coverage estimate. It is not reported separately.\n- **Multiple comparisons.** The 27 \"consistent with nominal\" checks are not adjusted for multiplicity, so roughly one pair could be misclassified by chance.\n\n## Limitations and open issues\n- **Distributions.** Only Gaussian, homoskedastic u and e were studied. Skewed or heavy-tailed errors, which are known to hurt few-cluster inference more, are not covered.\n- **Heterogeneity.** There is a single fixed pattern of cluster sizes (8/16/32/64) and the ICC is the same across clusters. The design does not vary the degree of size imbalance, and it has no cluster-specific variances or heterogeneous effects. Results may differ with balanced clusters or more extreme imbalance.\n- **Regressors.** Only an intercept (the mean) is estimated. Inference on regression slopes, leverage from regressors, treatment indicators that vary only at the cluster level, and few treated clusters are not studied.\n- **Other estimators and bootstrap variants.** The following are not evaluated:\n  - CR2/CR3 (bias-reduced linearisation) and Bell–McCaffrey or Satterthwaite degrees of freedom;\n  - the restricted (null-imposed) wild bootstrap and inverted tests;\n  - Webb six-point or Mammen weights;\n  - full enumeration of the 2^G weight vectors.\n- **Why WCB does poorly at G = 5 is not settled.** With Rademacher weights, the G = 5 bootstrap distribution has at most 16 distinct |t*| values. WCB's poor coverage there is at least partly caused by this discreteness, and Webb weights would probably behave differently. That is not tested here.\n- **Choice of quantile rule.** The WCB quantile uses numpy's linear interpolation. Another quantile rule, such as the 380th order statistic of 399, could shift WCB coverage slightly, especially at G = 5. This sensitivity was not run, to stay within the specified design.\n- **Scope of the grid.** Only G ∈ {5, 10, 30} and ICC ∈ {0, 0.2, 0.5} were run. Between grid points the behaviour is interpolation, not measurement.\nEOF\ncat > out/claims.json <<'EOF'\n[\n {\"id\":\"C1\",\"text\":\"11 of 27 (procedure, cell) pairs are failure regimes (coverage < 0.90): CR0 in all G<=10 cells, CR1 at G=5 with ICC 0.2 and 0.5, and WCB in all G=5 cells; no procedure fails at G=30.\",\"results_keys\":[\"failure_cells\",\"n_failure_cells\",\"coverage\"],\"status\":\"supported\",\"caveat\":\"cr0_G10_icc0.0 (0.8965) is only ~0.5 MC SE below 0.90.\"},\n {\"id\":\"C2\",\"text\":\"CR0 substantially underestimates Var(ybar) with few clusters (E[V]/Var 0.48-0.66 at G=5) and its coverage is 0.72-0.78 at G=5.\",\"results_keys\":[\"rel_bias_var\",\"exact_rel_bias_var_analytic\",\"coverage\"],\"status\":\"supported\",\"caveat\":\"MC bias estimates agree with analytic values within 2.5 MC SE.\"},\n {\"id\":\"C3\",\"text\":\"CR1 has the highest average coverage over the G=5 cells (0.8905 vs 0.8527 WCB and 0.7395 CR0) but remains below nominal there.\",\"results_keys\":[\"best_procedure_small_G\",\"avg_coverage_small_G\",\"coverage\"],\"status\":\"supported\",\"caveat\":\"Specific to Rademacher WCB with linear-interpolation quantile; G=5 Rademacher bootstrap is highly discrete.\"},\n {\"id\":\"C4\",\"text\":\"WCB gives the coverage closest to nominal at G=10 with ICC>0 and at G=30, and is consistent with nominal in all three G=30 cells.\",\"results_keys\":[\"coverage\",\"nominal_cells\"],\"status\":\"supported\",\"caveat\":\"Differences between WCB and CR1 at G=30, ICC=0 are within MC error.\"},\n {\"id\":\"C5\",\"text\":\"Only five pairs are consistent with nominal coverage (|p-0.95| <= 2 MC SE): cr1 at G=10 and G=30 with ICC=0, and wcb in all G=30 cells.\",\"results_keys\":[\"nominal_cells\",\"mc_se_coverage\"],\"status\":\"supported\",\"caveat\":\"No multiplicity adjustment across 27 comparisons.\"},\n {\"id\":\"C6\",\"text\":\"Monte Carlo SEs of coverage are 0.005-0.010 with R=2000.\",\"results_keys\":[\"mc_se_coverage\",\"R\"],\"status\":\"supported\",\"caveat\":\"\"},\n {\"id\":\"C7\",\"text\":\"The naive iid interval (baseline) covers ~0.95 at ICC=0 but only 0.29-0.49 when ICC>0.\",\"results_keys\":[\"baseline_iid_coverage\",\"baseline_iid_rel_bias_var\"],\"status\":\"supported\",\"caveat\":\"Supplementary baseline, not a required estimand.\"}\n]\nEOF\n~/wp29/venv/bin/python - <<'EOF'\nimport json\nr=json.load(open('out/results.json')); c=json.load(open('out/claims.json'))\nprint(all(k in r for x in c for k in x['results_keys']))\np=json.load(open('out/state/phases.json'))\np['P5']={\"status\":\"passed\",\"checks\":[\"report.md has 6 required sections\",\"claims.json keys all exist in results.json\",\"all report numbers taken from results.json\"],\"notes\":\"\"}\njson.dump(p,open('out/state/phases.json','w'),indent=1)\ns=open('out/report.md').read()\nprint([h for h in [\"## Question\",\"## Data and exclusions\",\"## Method\",\"## Results\",\"## Uncertainty\",\"## Limitations and open issues\"] if h not in s])\nEOF\necho \"- 2026-10-30 P5 passed: report.md and claims.json written; all phases passed.\" >> out/state/log.md; ls out out/state","description":"Write report, claims, finalize 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ran the full simulation: all 9 design cells × 2,000 replications, with B = 399 bootstrap draws per replication and the seeds from `design.csv`. All required files are written and every protocol phase passed. A clean re-run of `out/analysis.py` (about 2 seconds) gives a byte-identical `results.json`, and 32 consistency checks passed with none failing.\n\n**Coverage of the nominal 95% interval**\n\n| Clusters (G) | CR0 | CR1 | WCB |\n|---|---|---|---|\n| 5 | 0.72–0.78 | 0.88–0.91 | about 0.85 |\n| 10 | 0.85–0.90 | 0.91–0.94 | 0.93–0.94 |\n| 30 | 0.92–0.93 | 0.93–0.94 | 0.94–0.95 |\n\n- **Failure regimes:** 11 of the 27 (procedure, cell) pairs cover below 0.90:\n  - CR0 in all six cells with 10 or fewer clusters;\n  - CR1 at G = 5 with ICC 0.2 and 0.5;\n  - WCB in all three G = 5 cells.\n\n  Nothing fails at G = 30. One CR0 cell (G = 10, ICC = 0) is borderline at 0.8965, only half a standard error below 0.90.\n- **Best at G = 5:** CR1, with average coverage 0.8905, against 0.853 for WCB and 0.740 for CR0. It is still clearly below 95%.\n- **Consistent with 95%:** only five pairs: CR1 at G = 10 and G = 30 with ICC = 0, and WCB in all three G = 30 cells.\n- **Variance underestimation:** CR0's variance estimate averages 0.48–0.66 of the true variance at G = 5. CR1's averages 0.60–0.83, so it is still too small even after its correction factor.\n- **Precision:** Monte Carlo standard errors on coverage are 0.005–0.010. As a cross-check, the simulated CR0 bias matches a formula I derived for it within 2.5 standard errors in all 9 cells.\n- **Baseline:** an interval that ignores clustering covers about 0.95 when ICC = 0, but only 0.29–0.49 when ICC > 0.\n\n**Choices and limitations:**\n- With G = 5, the Rademacher bootstrap can produce at most 16 distinct values of the test statistic. This probably explains much of WCB's poor coverage there.\n- The task's \"0.95 empirical quantile\" can be computed more than one way. I used numpy's default rule and did not test alternatives, which could shift WCB coverage slightly at G = 5.\n- When ICC = 0, the cluster effect has zero variance, so it is simply zero in those cells.\n- The design does not cover non-normal errors, different cluster-size patterns, regressors beyond the mean, other variance estimators, or other bootstrap weights. The report discusses all of these.\n\n`results.json` also contains some extra keys beyond the required ones: the baseline, the formula-based bias values and per-cell diagnostics.\n\nFiles are in `out/`:\n- `results.json`\n- `table1.csv`\n- `figure1.png`\n- `report.md`\n- `claims.json`\n- `analysis.py`\n- `exclusions.csv`\n- `state/` (contract, data audit, checks, phase ledger, log)"}],"container":null,"stop_reason":"end_turn","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":6575,"cache_read_input_tokens":34668,"output_tokens":1260,"output_tokens_details":{"thinking_tokens":127},"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":6575},"inference_geo":"global","iterations":[],"speed":"standard","fallback_credit":null},"input_transformations":[],"diagnostics":null,"context_management":null},"apiBlockIndex":1,"requestId":"req_011CgXtFa67aJuuBU2NA1F2m","requestRef":"4696e717-08f1-4208-9803-41b5d6ef6ce7","requestedModel":"claude-opus-5-5","type":"assistant","uuid":"fa4c3bed-113d-4125-828b-a34f16dc24b5","timestamp":"2026-10-30T05:39:59.467Z","effort":"medium","perTurnEffort":"medium","userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RT0019","sessionId":"47ce3250-2468-470f-8e8f-38dfc848301c","version":"2.1.314","gitBranch":"HEAD"}129{"type":"last-prompt","lastPrompt":"Begin","leafUuid":"fa4c3bed-113d-4125-828b-a34f16dc24b5","sessionId":"47ce3250-2468-470f-8e8f-38dfc848301c"}130{"type":"cost-state","sessionId":"47ce3250-2468-470f-8e8f-38dfc848301c","totalCostUSD":0.7816714000000001,"totalAPIDuration":221587,"totalAPIDurationWithoutRetries":221575,"totalToolDuration":14265,"totalLinesAdded":0,"totalLinesRemoved":0,"totalDuration":235997,"startTime":1793338563495,"modelUsage":{"claude-opus-5-5":{"inputTokens":46,"outputTokens":24522,"thinkingTokens":2981,"cacheReadInputTokens":461762,"cacheCreationInputTokens":39739,"cacheCreationEphemeral5mInputTokens":39739,"cacheCreationEphemeral1hInputTokens":0,"webSearchRequests":0,"costUSD":0.7816714000000001}},"hasUnknownModelCost":false}131