spb/wp29_uqo

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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. 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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`.\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/RT0022","sessionId":"4a686388-81c9-4843-bd6d-090bcf8e9da0","version":"2.1.314","gitBranch":"HEAD"}11{"type":"tools-used","tools":["Bash"],"sessionId":"4a686388-81c9-4843-bd6d-090bcf8e9da0"}12{"parentUuid":"1de0c3d4-00cc-4ce6-8ad5-7e60afce476a","isSidechain":false,"message":{"model":"claude-opus-5-5","id":"msg_011CgXtD4rQvVVHT1Ncx4mfW","type":"message","role":"assistant","content":[{"type":"tool_use","id":"toolu_01UXiLjXEcBVwTAQRUJzvJ6G","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.\ndesign.csv\nREADME.md","is_error":false}]},"uuid":"70468611-800e-4918-82b4-b7b053fd5e9a","timestamp":"2026-10-30T05:39:15.818Z","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","is_error":false}]},"uuid":"56268dbd-d21c-48d0-9d91-aeec27fe1eb5","timestamp":"2026-10-30T05:39:17.316Z","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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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","stderr":"","interrupted":false,"isImage":false,"noOutputExpected":false},"sandboxed":false,"sourceToolAssistantUUID":"dc320c0b-1b4d-4475-9908-b0ceffcc0906","permissionDecision":{"decision":"accept","source":"config","reasonType":"rule","ruleSource":"cliArg"},"userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RT0022","sessionId":"4a686388-81c9-4843-bd6d-090bcf8e9da0","version":"2.1.314","gitBranch":"HEAD"}18{"type":"api-request-blob","sessionId":"4a686388-81c9-4843-bd6d-090bcf8e9da0","hash":"76b2bc7cffd3e4f29ed73cc0ee41ab49bd5f0fcd02dde08943f0f461037f22cd","message":{"role":"assistant","content":[{"type":"tool_use","id":"toolu_01EigWfWfUyKQ3P1CPyUw1Mi","name":"Bash","input":{"command":"cat 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-p out; cat > out/analysis.py <<'EOF'\n\"\"\"T2-simulation: coverage of CR0, CR1 and wild-cluster-bootstrap-t intervals for a mean.\nRun from workspace root: ~/wp29/venv/bin/python out/analysis.py\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\nOUT = \"out\"\ndesign = pd.read_csv(\"data/design.csv\")\nissues = []\n\ndef simulate_cell(row):\n    sizes = np.array([int(s) for s in str(row.cluster_sizes).split(\"|\")])\n    G, icc, R, B, mu = int(row.G), float(row.icc), int(row.R), int(row.B), float(row.mu)\n    N = int(sizes.sum())\n    if N != int(row.N) or len(sizes) != G:\n        issues.append({\"issue\": f\"{row.cell_id}: cluster_sizes inconsistent with G/N\",\n                       \"handling\": \"used cluster_sizes as given\"})\n    rng = np.random.default_rng(int(row.seed))\n    # data: all R replications at once (order: u then e, then bootstrap weights)\n    u = rng.normal(0.0, np.sqrt(icc), size=(R, G)) if icc > 0 else np.zeros((R, G))\n    e = rng.normal(0.0, np.sqrt(1.0 - icc), size=(R, N))\n    gid = np.repeat(np.arange(G), sizes)\n    y = mu + u[:, gid] + e                                   # R x N\n    ybar = y.mean(axis=1)                                    # R\n    resid = y - ybar[:, None]\n    S = np.zeros((R, G))\n    for g in range(G):\n        S[:, g] = resid[:, gid == g].sum(axis=1)             # cluster residual sums\n    V0 = (S ** 2).sum(axis=1) / N ** 2\n    V1 = G / (G - 1) * V0\n    # wild cluster bootstrap-t (Rademacher, unrestricted); statistics depend only on cluster sums:\n    # ybar* - ybar = sum_g w_g S_g / N ; bootstrap residual cluster sums = w_g S_g - n_g (ybar*-ybar)\n    W = rng.choice(np.array([-1.0, 1.0]), size=(R, B, G))\n    wS = W * S[:, None, :]                                   # R x B x G\n    d = wS.sum(axis=2) / N                                   # R x B\n    Sstar = wS - sizes[None, None, :] * d[:, :, None]\n    V1star = G / (G - 1) * (Sstar ** 2).sum(axis=2) / N ** 2\n    with np.errstate(divide=\"ignore\", invalid=\"ignore\"):\n        tstar = np.abs(d) / np.sqrt(V1star)\n    n_degen = int(np.sum(V1star <= 0))\n    tstar = np.where(V1star > 0, tstar, 0.0)\n    # 0.95 empirical quantile of |t*|: order statistic ceil(0.95*(B+1)) = 380th of 399\n    k = int(np.ceil(0.95 * (B + 1))) - 1\n    q = np.sort(tstar, axis=1)[:, k]\n    z = stats.norm.ppf(0.975)  # 1.95996...; task says 1.96\n    z = 1.96\n    tc = stats.t.ppf(0.975, G - 1)\n    hw = {\"cr0\": z * np.sqrt(V0), \"cr1\": tc * np.sqrt(V1), \"wcb\": q * np.sqrt(V1)}\n    var_exact = (icc * (sizes ** 2).sum() + (1 - icc) * N) / N ** 2\n    out = []\n    for est, h in hw.items():\n        cov = float(np.mean(np.abs(ybar - mu) <= h))\n        se = float(np.sqrt(cov * (1 - cov) / R))\n        rb = None\n        if est == \"cr0\": rb = float(V0.mean() / var_exact)\n        if est == \"cr1\": rb = float(V1.mean() / var_exact)\n        out.append(dict(estimator=est, cell_id=row.cell_id, G=G, icc=icc, coverage=cov,\n                        mc_se_coverage=se, rel_bias_var=rb, mean_ci_halfwidth=float(h.mean()), R=R))\n    extra = dict(cell_id=row.cell_id, n_degenerate_boot=n_degen,\n                 var_exact=var_exact, emp_var_ybar=float(ybar.var(ddof=1)),\n                 sd_V0=float(V0.std(ddof=1)), sd_V1=float(V1.std(ddof=1)),\n                 mean_q=float(q.mean()), t_crit=float(tc))\n    return out, extra\n\nrows, extras = [], []\nfor _, r in design.iterrows():\n    o, x = simulate_cell(r)\n    rows += o; extras.append(x)\n    print(r.cell_id, [(d[\"estimator\"], round(d[\"coverage\"], 4)) for d in o])\n\ntab = pd.DataFrame(rows)\ntab.to_csv(os.path.join(OUT, \"table1.csv\"), index=False)\npd.DataFrame(extras).to_csv(os.path.join(OUT, \"cell_diagnostics.csv\"), index=False)\n\nkey = lambda d: f\"{d['estimator']}_G{d['G']}_icc{d['icc']:.1f}\"\ncoverage = {key(d): d[\"coverage\"] for d in rows}\nmcse = {key(d): d[\"mc_se_coverage\"] for d in rows}\nrelb = {key(d): d[\"rel_bias_var\"] for d in rows if d[\"estimator\"] != \"wcb\"}\nfail = [key(d) for d in rows if d[\"coverage\"] < 0.90]\nnominal = [key(d) for d in rows if abs(d[\"coverage\"] - 0.95) <= 2 * d[\"mc_se_coverage\"]]\nsmall = tab[tab.G == 5].groupby(\"estimator\").coverage.mean()\nbest = str(small.idxmax())\n\nfor x in extras:\n    if x[\"n_degenerate_boot\"] > 0:\n        issues.append({\"issue\": f\"{x['cell_id']}: {x['n_degenerate_boot']} bootstrap draws with zero V*_CR1\",\n                       \"handling\": \"|t*| set to 0 for those draws\"})\nissues.append({\"issue\": \"With G=5 Rademacher weights there are only 2^5=32 distinct weight vectors \"\n               \"(16 distinct |t*| values up to sign), so the WCB critical value from B=399 draws is \"\n               \"a quantile of a highly discrete distribution\",\n               \"handling\": \"kept the specified procedure (B=399 random draws, no enumeration); noted in report\"})\n\nres = {\n    \"task_id\": \"T2-simulation\", \"R\": int(design.R.iloc[0]), \"B\": int(design.B.iloc[0]),\n    \"n_cells\": int(len(design)), \"coverage\": coverage, \"mc_se_coverage\": mcse,\n    \"rel_bias_var\": relb, \"failure_cells\": fail, \"n_failure_cells\": len(fail),\n    \"nominal_cells\": nominal, \"best_procedure_small_G\": best,\n    \"feasibility\": \"complete\", \"feasibility_reason\": None, \"data_issues\": issues,\n    \"n_input_rows\": {\"design.csv\": int(len(design))},\n    \"n_analysed\": int(design.R.sum()), \"random_seed\": int(design.seed.iloc[0]),\n}\nwith open(os.path.join(OUT, \"results.json\"), \"w\") as f:\n    json.dump(res, f, indent=2)\n\nfig, axes = plt.subplots(1, 3, figsize=(13, 4.2), sharey=True)\nfor ax, icc in zip(axes, sorted(tab.icc.unique())):\n    for est, m in zip([\"cr0\", \"cr1\", \"wcb\"], [\"o\", \"s\", \"^\"]):\n        s = tab[(tab.icc == icc) & (tab.estimator == est)].sort_values(\"G\")\n        ax.errorbar(s.G, s.coverage, yerr=1.96 * s.mc_se_coverage, marker=m, 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 0.90\")\n    ax.set_xscale(\"log\"); ax.set_xticks([5, 10, 30]); ax.set_xticklabels([\"5\", \"10\", \"30\"])\n    ax.set_title(f\"ICC = {icc:.1f}\"); ax.set_xlabel(\"number of clusters G\")\naxes[0].set_ylabel(\"coverage (±1.96 MC SE)\"); axes[0].legend(loc=\"lower right\", fontsize=8)\nfig.suptitle(\"Coverage of nominal 95% cluster-robust intervals for the mean (R=2000 per cell)\")\nfig.tight_layout(); fig.savefig(os.path.join(OUT, \"figure1.png\"), dpi=130)\nprint(json.dumps({k: res[k] for k in [\"failure_cells\", \"nominal_cells\", \"best_procedure_small_G\"]}, indent=1))\nEOF\ntime ~/wp29/venv/bin/python out/analysis.py","description":"Write and run simulation 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[('cr0', 0.7995), ('cr1', 0.9195), ('wcb', 0.87)]\nC2 [('cr0', 0.7195), ('cr1', 0.8815), ('wcb', 0.847)]\nC3 [('cr0', 0.7205), ('cr1', 0.8795), ('wcb', 0.8565)]\nC4 [('cr0', 0.877), ('cr1', 0.934), ('wcb', 0.9285)]\nC5 [('cr0', 0.859), ('cr1', 0.9165), ('wcb', 0.939)]\nC6 [('cr0', 0.853), ('cr1', 0.917), ('wcb', 0.9365)]\nC7 [('cr0', 0.935), ('cr1', 0.951), ('wcb', 0.9565)]\nC8 [('cr0', 0.924), ('cr1', 0.94), ('wcb', 0.9515)]\nC9 [('cr0', 0.916), ('cr1', 0.929), ('wcb', 0.9415)]\n{\n \"failure_cells\": [\n  \"cr0_G5_icc0.0\",\n  \"wcb_G5_icc0.0\",\n  \"cr0_G5_icc0.2\",\n  \"cr1_G5_icc0.2\",\n  \"wcb_G5_icc0.2\",\n  \"cr0_G5_icc0.5\",\n  \"cr1_G5_icc0.5\",\n  \"wcb_G5_icc0.5\",\n  \"cr0_G10_icc0.0\",\n  \"cr0_G10_icc0.2\",\n  \"cr0_G10_icc0.5\"\n ],\n \"nominal_cells\": [\n  \"cr1_G30_icc0.0\",\n  \"wcb_G30_icc0.0\",\n  \"cr1_G30_icc0.2\",\n  \"wcb_G30_icc0.2\",\n  \"wcb_G30_icc0.5\"\n ],\n \"best_procedure_small_G\": \"cr1\"\n}\n~/wp29/venv/bin/python out/analysis.py  1,89s user 0,15s 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\"cr1_G30_icc0.2\",\n  \"wcb_G30_icc0.2\",\n  \"wcb_G30_icc0.5\"\n ],\n \"best_procedure_small_G\": \"cr1\"\n}\n~/wp29/venv/bin/python out/analysis.py  1,89s user 0,15s system 99% cpu 2,043 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-p out; cat > out/analysis.py <<'EOF'\n\"\"\"T2-simulation: coverage of CR0, CR1 and wild-cluster-bootstrap-t intervals for a mean.\nRun from workspace root: ~/wp29/venv/bin/python out/analysis.py\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\nOUT = \"out\"\ndesign = pd.read_csv(\"data/design.csv\")\nissues = []\n\ndef simulate_cell(row):\n    sizes = np.array([int(s) for s in str(row.cluster_sizes).split(\"|\")])\n    G, icc, R, B, mu = int(row.G), float(row.icc), int(row.R), int(row.B), float(row.mu)\n    N = int(sizes.sum())\n    if N != int(row.N) or len(sizes) != G:\n        issues.append({\"issue\": f\"{row.cell_id}: cluster_sizes inconsistent with G/N\",\n                       \"handling\": \"used cluster_sizes as given\"})\n    rng = np.random.default_rng(int(row.seed))\n    # data: all R replications at once (order: u then e, then bootstrap weights)\n    u = rng.normal(0.0, np.sqrt(icc), size=(R, G)) if icc > 0 else np.zeros((R, G))\n    e = rng.normal(0.0, np.sqrt(1.0 - icc), size=(R, N))\n    gid = np.repeat(np.arange(G), sizes)\n    y = mu + u[:, gid] + e                                   # R x N\n    ybar = y.mean(axis=1)                                    # R\n    resid = y - ybar[:, None]\n    S = np.zeros((R, G))\n    for g in range(G):\n        S[:, g] = resid[:, gid == g].sum(axis=1)             # cluster residual sums\n    V0 = (S ** 2).sum(axis=1) / N ** 2\n    V1 = G / (G - 1) * V0\n    # wild cluster bootstrap-t (Rademacher, unrestricted); statistics depend only on cluster sums:\n    # ybar* - ybar = sum_g w_g S_g / N ; bootstrap residual cluster sums = w_g S_g - n_g (ybar*-ybar)\n    W = rng.choice(np.array([-1.0, 1.0]), size=(R, B, G))\n    wS = W * S[:, None, :]                                   # R x B x G\n    d = wS.sum(axis=2) / N                                   # R x B\n    Sstar = wS - sizes[None, None, :] * d[:, :, None]\n    V1star = G / (G - 1) * (Sstar ** 2).sum(axis=2) / N ** 2\n    with np.errstate(divide=\"ignore\", invalid=\"ignore\"):\n        tstar = np.abs(d) / np.sqrt(V1star)\n    n_degen = int(np.sum(V1star <= 0))\n    tstar = np.where(V1star > 0, tstar, 0.0)\n    # 0.95 empirical quantile of |t*|: order statistic ceil(0.95*(B+1)) = 380th of 399\n    k = int(np.ceil(0.95 * (B + 1))) - 1\n    q = np.sort(tstar, axis=1)[:, k]\n    z = stats.norm.ppf(0.975)  # 1.95996...; task says 1.96\n    z = 1.96\n    tc = stats.t.ppf(0.975, G - 1)\n    hw = {\"cr0\": z * np.sqrt(V0), \"cr1\": tc * np.sqrt(V1), \"wcb\": q * np.sqrt(V1)}\n    var_exact = (icc * (sizes ** 2).sum() + (1 - icc) * N) / N ** 2\n    out = []\n    for est, h in hw.items():\n        cov = float(np.mean(np.abs(ybar - mu) <= h))\n        se = float(np.sqrt(cov * (1 - cov) / R))\n        rb = None\n        if est == \"cr0\": rb = float(V0.mean() / var_exact)\n        if est == \"cr1\": rb = float(V1.mean() / var_exact)\n        out.append(dict(estimator=est, cell_id=row.cell_id, G=G, icc=icc, coverage=cov,\n                        mc_se_coverage=se, rel_bias_var=rb, mean_ci_halfwidth=float(h.mean()), R=R))\n    extra = dict(cell_id=row.cell_id, n_degenerate_boot=n_degen,\n                 var_exact=var_exact, emp_var_ybar=float(ybar.var(ddof=1)),\n                 sd_V0=float(V0.std(ddof=1)), sd_V1=float(V1.std(ddof=1)),\n                 mean_q=float(q.mean()), t_crit=float(tc))\n    return out, extra\n\nrows, extras = [], []\nfor _, r in design.iterrows():\n    o, x = simulate_cell(r)\n    rows += o; extras.append(x)\n    print(r.cell_id, [(d[\"estimator\"], round(d[\"coverage\"], 4)) for d in o])\n\ntab = pd.DataFrame(rows)\ntab.to_csv(os.path.join(OUT, \"table1.csv\"), index=False)\npd.DataFrame(extras).to_csv(os.path.join(OUT, \"cell_diagnostics.csv\"), index=False)\n\nkey = lambda d: f\"{d['estimator']}_G{d['G']}_icc{d['icc']:.1f}\"\ncoverage = {key(d): d[\"coverage\"] for d in rows}\nmcse = {key(d): d[\"mc_se_coverage\"] for d in rows}\nrelb = {key(d): d[\"rel_bias_var\"] for d in rows if d[\"estimator\"] != \"wcb\"}\nfail = [key(d) for d in rows if d[\"coverage\"] < 0.90]\nnominal = [key(d) for d in rows if abs(d[\"coverage\"] - 0.95) <= 2 * d[\"mc_se_coverage\"]]\nsmall = tab[tab.G == 5].groupby(\"estimator\").coverage.mean()\nbest = str(small.idxmax())\n\nfor x in extras:\n    if x[\"n_degenerate_boot\"] > 0:\n        issues.append({\"issue\": f\"{x['cell_id']}: {x['n_degenerate_boot']} bootstrap draws with zero V*_CR1\",\n                       \"handling\": \"|t*| set to 0 for those draws\"})\nissues.append({\"issue\": \"With G=5 Rademacher weights there are only 2^5=32 distinct weight vectors \"\n               \"(16 distinct |t*| values up to sign), so the WCB critical value from B=399 draws is \"\n               \"a quantile of a highly discrete distribution\",\n               \"handling\": \"kept the specified procedure (B=399 random draws, no enumeration); noted in report\"})\n\nres = {\n    \"task_id\": \"T2-simulation\", \"R\": int(design.R.iloc[0]), \"B\": int(design.B.iloc[0]),\n    \"n_cells\": int(len(design)), \"coverage\": coverage, \"mc_se_coverage\": mcse,\n    \"rel_bias_var\": relb, \"failure_cells\": fail, \"n_failure_cells\": len(fail),\n    \"nominal_cells\": nominal, \"best_procedure_small_G\": best,\n    \"feasibility\": \"complete\", \"feasibility_reason\": None, \"data_issues\": issues,\n    \"n_input_rows\": {\"design.csv\": int(len(design))},\n    \"n_analysed\": int(design.R.sum()), \"random_seed\": int(design.seed.iloc[0]),\n}\nwith open(os.path.join(OUT, \"results.json\"), \"w\") as f:\n    json.dump(res, f, indent=2)\n\nfig, axes = plt.subplots(1, 3, figsize=(13, 4.2), sharey=True)\nfor ax, icc in zip(axes, sorted(tab.icc.unique())):\n    for est, m in zip([\"cr0\", \"cr1\", \"wcb\"], [\"o\", \"s\", \"^\"]):\n        s = tab[(tab.icc == icc) & (tab.estimator == est)].sort_values(\"G\")\n        ax.errorbar(s.G, 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> out/report.md <<'EOF'\n# Finite-sample coverage of CR0, CR1 and wild-cluster-bootstrap-t intervals for a mean (T2-simulation)\n\nAll numbers below are produced by `out/analysis.py` (run `~/wp29/venv/bin/python out/analysis.py` from the workspace root; ~2 s runtime, deterministic given the seeds in `data/design.csv`). They are in `out/results.json`, `out/table1.csv` and `out/cell_diagnostics.csv`; the figure is `out/figure1.png`.\n\n## Question\nWhen only a few clusters are available, how close to 95% is the actual coverage of three cluster-robust intervals for the population mean μ, estimated by the sample mean ȳ: CR0 with a normal critical value, CR1 with a t(G−1) critical value, and the wild cluster bootstrap-t (WCB, Rademacher, unrestricted)? Also: how biased are the CR0/CR1 variance estimators relative to the exact Var(ȳ), and in which (procedure, cell) pairs does coverage drop below 0.90?\n\n## Data and exclusions\nNo observed data are used. The only input is `data/design.csv`, which has 9 rows (cells C1–C9: G ∈ {5,10,30} × ICC ∈ {0,0.2,0.5}). Cluster sizes repeat the pattern 8,16,32,64, so N = 128, 264 and 864. The script checked that the listed cluster sizes match G and N in every row, and they all do. Nothing was excluded. Each cell uses R = 2000 replications and B = 399 bootstrap draws, so 18,000 replications were simulated in total. No bootstrap draw produced a zero bootstrap variance.\n\n## Method\n- **Data-generating process (DGP):** y_ig = μ + u_g + e_ig, with u_g ~ N(0, ICC) and e_ig ~ N(0, 1−ICC). Each cell uses `numpy.random.default_rng(seed)` with that cell's seed. All R replications are drawn in one go in this order: u (R×G; skipped when ICC = 0, where u ≡ 0), then e (R×N), then the Rademacher weights (R×B×G).\n- **CR0:** V̂ = Σ_g(Σ_i ê_ig)²/N²; the interval is ȳ ± 1.96√V̂.\n- **CR1:** V̂ = G/(G−1)·V̂_CR0; the interval is ȳ ± t_{0.975,G−1}√V̂.\n- **WCB:** y* = ȳ + w_g ê_ig. Every bootstrap quantity depends only on the cluster residual sums S_g:\n  - ȳ* − ȳ = Σ w_g S_g / N;\n  - the bootstrap residual cluster sums are w_g S_g − n_g(ȳ* − ȳ);\n  - t* = (ȳ* − ȳ)/√V̂*_CR1. This is fully vectorised and algebraically identical to recomputing everything on y*.\n  - The critical value q is the \"0.95 empirical quantile of |t*|\". It was implemented as the ⌈0.95(B+1)⌉ = 380th order statistic of the 399 values of |t*|, the usual bootstrap convention (documented choice). The interval is ȳ ± q√V̂_CR1.\n- **Coverage, MC SE and relative bias:** coverage is the share of R intervals that contain μ = 0, and its MC SE is √(p̂(1−p̂)/R). Relative bias is mean(V̂)/Var(ȳ), using the exact Var(ȳ) = [ICC·Σn_g² + (1−ICC)N]/N².\n- **Classification:** a pair is a failure when p̂ < 0.90. It is \"consistent with nominal\" when |p̂ − 0.95| ≤ 2·MC SE.\n- **Sanity check:** in every cell, the Monte Carlo variance of ȳ is within sampling error of the exact Var(ȳ). The ratios run from 0.93 to 1.04; with R = 2000 the sampling SD of this ratio is about 0.03.\n\n## Results\nCoverage (MC SE in parentheses) and relative variance bias E[V̂]/Var(ȳ):\n\n| G | ICC | CR0 cov | CR1 cov | WCB cov | CR0 rel. bias | CR1 rel. bias |\n|---|---|---|---|---|---|---|\n| 5 | 0.0 | 0.800 (0.009) | 0.920 (0.006) | 0.870 (0.008) | 0.654 | 0.818 |\n| 5 | 0.2 | 0.720 (0.010) | 0.882 (0.007) | 0.847 (0.008) | 0.507 | 0.634 |\n| 5 | 0.5 | 0.721 (0.010) | 0.880 (0.007) | 0.857 (0.008) | 0.505 | 0.632 |\n| 10 | 0.0 | 0.877 (0.007) | 0.934 (0.006) | 0.929 (0.006) | 0.853 | 0.948 |\n| 10 | 0.2 | 0.859 (0.008) | 0.917 (0.006) | 0.939 (0.005) | 0.760 | 0.844 |\n| 10 | 0.5 | 0.853 (0.008) | 0.917 (0.006) | 0.937 (0.005) | 0.767 | 0.852 |\n| 30 | 0.0 | 0.935 (0.006) | 0.951 (0.005) | 0.957 (0.005) | 0.940 | 0.972 |\n| 30 | 0.2 | 0.924 (0.006) | 0.940 (0.005) | 0.952 (0.005) | 0.919 | 0.950 |\n| 30 | 0.5 | 0.916 (0.006) | 0.929 (0.006) | 0.942 (0.005) | 0.915 | 0.947 |\n\nMain findings:\n1. **Failure regimes (11 of 27 pairs below 0.90):**\n   - CR0 fails in all G = 5 and all G = 10 cells (coverage 0.72–0.88).\n   - CR1 fails at G = 5 with ICC = 0.2 and 0.5 (about 0.88).\n   - WCB fails in all three G = 5 cells (0.85–0.87).\n   - No procedure fails at G = 30.\n2. **Consistent with nominal coverage:** only 5 pairs, all at G = 30: CR1 at ICC 0 and 0.2, and WCB at ICC 0, 0.2 and 0.5. CR0 is never consistent with nominal, not even at G = 30, where it under-covers by 1.5–3.4 points.\n3. **Small G:** at G = 5, CR1 has the highest average coverage (best_procedure_small_G = `cr1`; mean 0.894, compared with 0.858 for WCB and 0.747 for CR0). At G = 10, WCB is closest to nominal when ICC > 0 (0.937–0.939, compared with 0.917 for CR1).\n4. **Variance bias:** the sandwich variance is biased downward in every cell.\n   - At G = 5 with ICC > 0, CR0 captures only about half of Var(ȳ) and CR1 about 63%.\n   - The bias shrinks with G. At G = 30, CR1 reaches 0.95–0.97, CR0 0.92–0.94.\n   - The bias is worse with ICC > 0 than with ICC = 0. Because cluster sizes are unequal (8 to 64), the large clusters dominate Var(ȳ) when ICC > 0, which makes the effective number of clusters smaller than G.\n5. **WCB at G = 5:** WCB under-covers even though its average half-width is larger than CR1's (for example 0.88 vs 0.53 at ICC = 0.2), and its average critical value (3.1–4.2) is above t_{0.975,4} = 2.78. So the bootstrap q is large on average but not where it is needed. A plausible explanation, which I did not test separately, involves two features:\n   - With G = 5 there are only 2⁵ = 32 Rademacher weight vectors (16 distinct |t*| values), so the bootstrap distribution is very coarse.\n   - q tends to be small in exactly the samples where one large cluster drives a large |t|.\n\n## Uncertainty\n- **Coverage:** each coverage estimate is a binomial proportion from R = 2000 independent replications. Its MC SE ranges from 0.0046 (coverage near 0.96) to 0.010 (coverage near 0.72), and is reported for every pair in `table1.csv` and `results.json`. A 95% MC interval is about ±0.01–0.02.\n- **Robust vs borderline conclusions:** the failure classifications at G = 5 for CR0 and WCB, and for CR0 at G = 10, are many SEs below 0.90, so they are robust. Three are borderline:\n  - CR1 at G = 5 with ICC 0.2 and 0.5 (0.882 and 0.880) is about 2.5–3 SE below 0.90.\n  - CR1 at G = 5 with ICC 0 (0.920) is about 3 SE above it.\n  - The 2·MC SE \"nominal\" band is a pointwise criterion that is not adjusted for multiplicity across 27 pairs. Pairs near its edge, such as CR1 at G = 30 with ICC 0.2 (0.940, SE 0.0053) or CR1 at G = 10 with ICC 0 (0.934), could be classified differently with another seed.\n- **Variance bias:** the MC SE of the relative-bias estimates is sd(V̂)/(√R·Var(ȳ)). It is about 0.012–0.016 at G = 5 and 10, and about 0.007–0.009 at G = 30 (`cell_diagnostics.csv`). All the downward biases reported above are therefore far beyond Monte Carlo noise, except the difference between CR1 at G = 30 and 1, which is 3–6 SE.\n- **Bootstrap error:** the WCB critical values carry extra simulation noise from using B = 399 rather than infinitely many draws. This noise is already included in the binomial MC SE of WCB coverage, but it means the results describe WCB with B = 399, not the ideal bootstrap.\n\n## Limitations and open issues\n- **Distributions:** only Gaussian cluster effects and errors were studied. Skewed or heavy-tailed u_g or e_ig, which typically hurt small-G inference more, are not covered.\n- **Heterogeneity:** variances and ICC are the same in every cluster, with no correlation between cluster size and cluster effect. Only one pattern of cluster-size imbalance was used (8,16,32,64 repeated), and the G = 5 design has two clusters of size 8. Balanced clusters, or stronger imbalance, could change the ranking.\n- **Regressors:** the estimand is a plain mean. Regression coefficients, few treated clusters and leverage effects (where CR2/CR3 and restricted WCB matter) are not studied.\n- **Other weights and variants:** WCB used only Rademacher weights, unrestricted (no null imposed), with B = 399 random draws. Webb 6-point or Mammen weights, null-restricted WCB (WCR), full enumeration of the 32 weight vectors at G = 5, CR2/Bell–McCaffrey degrees of freedom, and CR3/jackknife were not evaluated.\n- **Other choices:**\n  - The bootstrap quantile was taken as the 380th order statistic of 399. Other quantile definitions differ slightly.\n  - CR0 uses 1.96 as specified rather than the exact 1.95996.\n- **Unchecked explanation:** the explanation offered for WCB's behaviour at G = 5 has not been checked by a separate analysis.\n- **Design limits:** only G ∈ {5,10,30} and ICC ∈ {0,0.2,0.5} were studied. The transition between G = 10 and 30 is not resolved.\nEOF\ncat > out/claims.json <<'EOF'\n[\n {\"id\":\"C1\",\"text\":\"CR0 with normal critical value under-covers in every cell and fails (coverage < 0.90) in all G=5 and G=10 cells.\",\"results_keys\":[\"coverage\",\"failure_cells\"],\"status\":\"supported\",\"caveat\":\"Gaussian DGP, one cluster-size pattern.\"},\n {\"id\":\"C2\",\"text\":\"All three procedures fail at G=5 except CR1 with ICC=0 (0.920); no procedure fails at G=30.\",\"results_keys\":[\"coverage\",\"failure_cells\",\"n_failure_cells\"],\"status\":\"supported\",\"caveat\":\"CR1 G=5 ICC 0.2/0.5 are only ~2.5-3 MC SE below 0.90.\"},\n {\"id\":\"C3\",\"text\":\"CR1 has the highest average coverage over the G=5 cells.\",\"results_keys\":[\"best_procedure_small_G\",\"coverage\"],\"status\":\"supported\",\"caveat\":\"WCB implemented with Rademacher weights and B=399; with G=5 only 32 distinct weight vectors exist.\"},\n {\"id\":\"C4\",\"text\":\"CR0 and CR1 variance estimators are biased downward in all cells, capturing only ~50% (CR0) and ~63% (CR1) of Var(ybar) at G=5 with ICC>0, approaching 0.92-0.97 at G=30.\",\"results_keys\":[\"rel_bias_var\"],\"status\":\"supported\",\"caveat\":\"MC SE of relative bias 0.007-0.016.\"},\n {\"id\":\"C5\",\"text\":\"Only 5 pairs are consistent with nominal coverage, all at G=30 (CR1 at ICC 0 and 0.2, WCB at all ICC).\",\"results_keys\":[\"nominal_cells\",\"coverage\",\"mc_se_coverage\"],\"status\":\"supported\",\"caveat\":\"Pointwise 2-SE criterion, no multiplicity adjustment; borderline pairs could flip under another seed.\"},\n {\"id\":\"C6\",\"text\":\"At G=10 with ICC>0, WCB is closest to nominal (0.937-0.939) versus CR1 (0.917).\",\"results_keys\":[\"coverage\"],\"status\":\"supported\",\"caveat\":\"Differences of ~2 points, roughly 2.5-3 combined MC SE.\"},\n {\"id\":\"C7\",\"text\":\"WCB under-coverage at G=5 is due to discreteness of the Rademacher bootstrap distribution and q being small when one large cluster dominates.\",\"results_keys\":[],\"status\":\"not_evaluated\",\"caveat\":\"Plausible mechanism, not tested separately.\"}\n]\nEOF\n~/wp29/venv/bin/python -c \"import json;json.load(open('out/claims.json'));print('ok')\"; ls out","description":"Write report and 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> out/report.md <<'EOF'\n# Finite-sample coverage of CR0, CR1 and wild-cluster-bootstrap-t intervals for a mean (T2-simulation)\n\nAll numbers below are produced by `out/analysis.py` (run `~/wp29/venv/bin/python out/analysis.py` from the workspace root; ~2 s runtime, deterministic given the seeds in `data/design.csv`). They are in `out/results.json`, `out/table1.csv` and `out/cell_diagnostics.csv`; the figure is `out/figure1.png`.\n\n## Question\nWhen only a few clusters are available, how close to 95% is the actual coverage of three cluster-robust intervals for the population mean μ, estimated by the sample mean ȳ: CR0 with a normal critical value, CR1 with a t(G−1) critical value, and the wild cluster bootstrap-t (WCB, Rademacher, unrestricted)? Also: how biased are the CR0/CR1 variance estimators relative to the exact Var(ȳ), and in which (procedure, cell) pairs does coverage drop below 0.90?\n\n## Data and exclusions\nNo observed data are used. The only input is `data/design.csv`, which has 9 rows (cells C1–C9: G ∈ {5,10,30} × ICC ∈ {0,0.2,0.5}). Cluster sizes repeat the pattern 8,16,32,64, so N = 128, 264 and 864. The script checked that the listed cluster sizes match G and N in every row, and they all do. Nothing was excluded. Each cell uses R = 2000 replications and B = 399 bootstrap draws, so 18,000 replications were simulated in total. No bootstrap draw produced a zero bootstrap variance.\n\n## Method\n- **Data-generating process (DGP):** y_ig = μ + u_g + e_ig, with u_g ~ N(0, ICC) and e_ig ~ N(0, 1−ICC). Each cell uses `numpy.random.default_rng(seed)` with that cell's seed. All R replications are drawn in one go in this order: u (R×G; skipped when ICC = 0, where u ≡ 0), then e (R×N), then the Rademacher weights (R×B×G).\n- **CR0:** V̂ = Σ_g(Σ_i ê_ig)²/N²; the interval is ȳ ± 1.96√V̂.\n- **CR1:** V̂ = G/(G−1)·V̂_CR0; the interval is ȳ ± t_{0.975,G−1}√V̂.\n- **WCB:** y* = ȳ + w_g ê_ig. Every bootstrap quantity depends only on the cluster residual sums S_g:\n  - ȳ* − ȳ = Σ w_g S_g / N;\n  - the bootstrap residual cluster sums are w_g S_g − n_g(ȳ* − ȳ);\n  - t* = (ȳ* − ȳ)/√V̂*_CR1. This is fully vectorised and algebraically identical to recomputing everything on y*.\n  - The critical value q is the \"0.95 empirical quantile of |t*|\". It was implemented as the ⌈0.95(B+1)⌉ = 380th order statistic of the 399 values of |t*|, the usual bootstrap convention (documented choice). The interval is ȳ ± q√V̂_CR1.\n- **Coverage, MC SE and relative bias:** coverage is the share of R intervals that contain μ = 0, and its MC SE is √(p̂(1−p̂)/R). Relative bias is mean(V̂)/Var(ȳ), using the exact Var(ȳ) = [ICC·Σn_g² + (1−ICC)N]/N².\n- **Classification:** a pair is a failure when p̂ < 0.90. It is \"consistent with nominal\" when |p̂ − 0.95| ≤ 2·MC SE.\n- **Sanity check:** in every cell, the Monte Carlo variance of ȳ is within sampling error of the exact Var(ȳ). The ratios run from 0.93 to 1.04; with R = 2000 the sampling SD of this ratio is about 0.03.\n\n## Results\nCoverage (MC SE in parentheses) and relative variance bias E[V̂]/Var(ȳ):\n\n| G | ICC | CR0 cov | CR1 cov | WCB cov | CR0 rel. bias | CR1 rel. bias |\n|---|---|---|---|---|---|---|\n| 5 | 0.0 | 0.800 (0.009) | 0.920 (0.006) | 0.870 (0.008) | 0.654 | 0.818 |\n| 5 | 0.2 | 0.720 (0.010) | 0.882 (0.007) | 0.847 (0.008) | 0.507 | 0.634 |\n| 5 | 0.5 | 0.721 (0.010) | 0.880 (0.007) | 0.857 (0.008) | 0.505 | 0.632 |\n| 10 | 0.0 | 0.877 (0.007) | 0.934 (0.006) | 0.929 (0.006) | 0.853 | 0.948 |\n| 10 | 0.2 | 0.859 (0.008) | 0.917 (0.006) | 0.939 (0.005) | 0.760 | 0.844 |\n| 10 | 0.5 | 0.853 (0.008) | 0.917 (0.006) | 0.937 (0.005) | 0.767 | 0.852 |\n| 30 | 0.0 | 0.935 (0.006) | 0.951 (0.005) | 0.957 (0.005) | 0.940 | 0.972 |\n| 30 | 0.2 | 0.924 (0.006) | 0.940 (0.005) | 0.952 (0.005) | 0.919 | 0.950 |\n| 30 | 0.5 | 0.916 (0.006) | 0.929 (0.006) | 0.942 (0.005) | 0.915 | 0.947 |\n\nMain findings:\n1. **Failure regimes (11 of 27 pairs below 0.90):**\n   - CR0 fails in all G = 5 and all G = 10 cells (coverage 0.72–0.88).\n   - CR1 fails at G = 5 with ICC = 0.2 and 0.5 (about 0.88).\n   - WCB fails in all three G = 5 cells (0.85–0.87).\n   - No procedure fails at G = 30.\n2. **Consistent with nominal coverage:** only 5 pairs, all at G = 30: CR1 at ICC 0 and 0.2, and WCB at ICC 0, 0.2 and 0.5. CR0 is never consistent with nominal, not even at G = 30, where it under-covers by 1.5–3.4 points.\n3. **Small G:** at G = 5, CR1 has the highest average coverage (best_procedure_small_G = `cr1`; mean 0.894, compared with 0.858 for WCB and 0.747 for CR0). At G = 10, WCB is closest to nominal when ICC > 0 (0.937–0.939, compared with 0.917 for CR1).\n4. **Variance bias:** the sandwich variance is biased downward in every cell.\n   - At G = 5 with ICC > 0, CR0 captures only about half of Var(ȳ) and CR1 about 63%.\n   - The bias shrinks with G. At G = 30, CR1 reaches 0.95–0.97, CR0 0.92–0.94.\n   - The bias is worse with ICC > 0 than with ICC = 0. Because cluster sizes are unequal (8 to 64), the large clusters dominate Var(ȳ) when ICC > 0, which makes the effective number of clusters smaller than G.\n5. **WCB at G = 5:** WCB under-covers even though its average half-width is larger than CR1's (for example 0.88 vs 0.53 at ICC = 0.2), and its average critical value (3.1–4.2) is above t_{0.975,4} = 2.78. So the bootstrap q is large on average but not where it is needed. A plausible explanation, which I did not test separately, involves two features:\n   - With G = 5 there are only 2⁵ = 32 Rademacher weight vectors (16 distinct |t*| values), so the bootstrap distribution is very coarse.\n   - q tends to be small in exactly the samples where one large cluster drives a large |t|.\n\n## Uncertainty\n- **Coverage:** each coverage estimate is a binomial proportion from R = 2000 independent replications. Its MC SE ranges from 0.0046 (coverage near 0.96) to 0.010 (coverage near 0.72), and is reported for every pair in `table1.csv` and `results.json`. A 95% MC interval is about ±0.01–0.02.\n- **Robust vs borderline conclusions:** the failure classifications at G = 5 for CR0 and WCB, and for CR0 at G = 10, are many SEs below 0.90, so they are robust. Three are borderline:\n  - CR1 at G = 5 with ICC 0.2 and 0.5 (0.882 and 0.880) is about 2.5–3 SE below 0.90.\n  - CR1 at G = 5 with ICC 0 (0.920) is about 3 SE above it.\n  - The 2·MC SE \"nominal\" band is a pointwise criterion that is not adjusted for multiplicity across 27 pairs. Pairs near its edge, such as CR1 at G = 30 with ICC 0.2 (0.940, SE 0.0053) or CR1 at G = 10 with ICC 0 (0.934), could be classified differently with another seed.\n- **Variance bias:** the MC SE of the relative-bias estimates is sd(V̂)/(√R·Var(ȳ)). It is about 0.012–0.016 at G = 5 and 10, and about 0.007–0.009 at G = 30 (`cell_diagnostics.csv`). All the downward biases reported above are therefore far beyond Monte Carlo noise, except the difference between CR1 at G = 30 and 1, which is 3–6 SE.\n- **Bootstrap error:** the WCB critical values carry extra simulation noise from using B = 399 rather than infinitely many draws. This noise is already included in the binomial MC SE of WCB coverage, but it means the results describe WCB with B = 399, not the ideal bootstrap.\n\n## Limitations and open issues\n- **Distributions:** only Gaussian cluster effects and errors were studied. Skewed or heavy-tailed u_g or e_ig, which typically hurt small-G inference more, are not covered.\n- **Heterogeneity:** variances and ICC are the same in every cluster, with no correlation between cluster size and cluster effect. Only one pattern of cluster-size imbalance was used (8,16,32,64 repeated), and the G = 5 design has two clusters of size 8. Balanced clusters, or stronger imbalance, could change the ranking.\n- **Regressors:** the estimand is a plain mean. Regression coefficients, few treated clusters and leverage effects (where CR2/CR3 and restricted WCB matter) are not studied.\n- **Other weights and variants:** WCB used only Rademacher weights, unrestricted (no null imposed), with B = 399 random draws. Webb 6-point or Mammen weights, null-restricted WCB (WCR), full enumeration of the 32 weight vectors at G = 5, CR2/Bell–McCaffrey degrees of freedom, and CR3/jackknife were not evaluated.\n- **Other choices:**\n  - The bootstrap quantile was taken as the 380th order statistic of 399. Other quantile definitions differ slightly.\n  - CR0 uses 1.96 as specified rather than the exact 1.95996.\n- **Unchecked explanation:** the explanation offered for WCB's behaviour at G = 5 has not been checked by a separate analysis.\n- **Design limits:** only G ∈ {5,10,30} and ICC ∈ {0,0.2,0.5} were studied. The transition between G = 10 and 30 is not resolved.\nEOF\ncat > out/claims.json <<'EOF'\n[\n {\"id\":\"C1\",\"text\":\"CR0 with normal critical value under-covers in every cell and fails (coverage < 0.90) in all G=5 and G=10 cells.\",\"results_keys\":[\"coverage\",\"failure_cells\"],\"status\":\"supported\",\"caveat\":\"Gaussian DGP, one cluster-size pattern.\"},\n {\"id\":\"C2\",\"text\":\"All three procedures fail at G=5 except CR1 with ICC=0 (0.920); no procedure fails at G=30.\",\"results_keys\":[\"coverage\",\"failure_cells\",\"n_failure_cells\"],\"status\":\"supported\",\"caveat\":\"CR1 G=5 ICC 0.2/0.5 are only ~2.5-3 MC SE below 0.90.\"},\n {\"id\":\"C3\",\"text\":\"CR1 has the highest average coverage over the G=5 cells.\",\"results_keys\":[\"best_procedure_small_G\",\"coverage\"],\"status\":\"supported\",\"caveat\":\"WCB implemented with Rademacher weights and B=399; with G=5 only 32 distinct weight vectors exist.\"},\n {\"id\":\"C4\",\"text\":\"CR0 and CR1 variance estimators are biased downward in all cells, capturing only ~50% (CR0) and ~63% (CR1) of Var(ybar) at G=5 with ICC>0, approaching 0.92-0.97 at G=30.\",\"results_keys\":[\"rel_bias_var\"],\"status\":\"supported\",\"caveat\":\"MC SE of relative bias 0.007-0.016.\"},\n {\"id\":\"C5\",\"text\":\"Only 5 pairs are consistent with nominal coverage, all at G=30 (CR1 at ICC 0 and 0.2, WCB at all ICC).\",\"results_keys\":[\"nominal_cells\",\"coverage\",\"mc_se_coverage\"],\"status\":\"supported\",\"caveat\":\"Pointwise 2-SE criterion, no multiplicity adjustment; borderline pairs could flip under another seed.\"},\n {\"id\":\"C6\",\"text\":\"At G=10 with ICC>0, WCB is closest to nominal (0.937-0.939) versus CR1 (0.917).\",\"results_keys\":[\"coverage\"],\"status\":\"supported\",\"caveat\":\"Differences of ~2 points, roughly 2.5-3 combined MC SE.\"},\n {\"id\":\"C7\",\"text\":\"WCB under-coverage at G=5 is due to discreteness of the Rademacher bootstrap distribution and q being small when one large cluster dominates.\",\"results_keys\":[],\"status\":\"not_evaluated\",\"caveat\":\"Plausible mechanism, not tested separately.\"}\n]\nEOF\n~/wp29/venv/bin/python -c \"import json;json.load(open('out/claims.json'));print('ok')\"; ls out","description":"Write report and 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ran the whole simulation as specified: all 9 cells, R = 2000 replications and B = 399 bootstrap draws per cell, 18,000 replications in total. All required files are written, and `out/analysis.py` regenerates the results in about 2 seconds, giving identical output on a second run.\n\n**What the simulation shows:**\n- **The usual interval with a normal critical value (CR0) is unreliable:** it falls below 95% in every cell. Coverage is 0.72–0.88 at G = 5 and 10, and still only 0.916–0.935 at G = 30.\n- **Failures:** 11 of the 27 (procedure, cell) pairs cover below 0.90:\n  - CR0 in all G = 5 and G = 10 cells;\n  - CR1 at G = 5 with ICC 0.2 and 0.5 (about 0.88);\n  - the wild cluster bootstrap (WCB) in all three G = 5 cells (0.85–0.87).\n  - Nothing fails at G = 30.\n- **Close to 95%:** only 5 pairs are within two Monte Carlo standard errors of 0.95, all at G = 30: CR1 at ICC 0 and 0.2, and WCB at all three ICC values.\n- **Best with 5 clusters:** CR1 has the highest average coverage at G = 5 (0.894, against 0.858 for WCB and 0.747 for CR0). At G = 10 with ICC above 0, WCB comes closest to 95% (about 0.94).\n- **Variance estimates are too small in every cell.** At G = 5 with ICC above 0, CR0 captures about half of the true variance of the mean and CR1 about 63%. At G = 30 this improves to roughly 0.92–0.97.\n- **Monte Carlo error is small relative to these gaps:** about 0.005–0.010 for coverage and 0.007–0.016 for the variance ratios. The report flags the classifications that sit near a threshold, such as CR1 at G = 5 with ICC 0.2 and 0.5.\n- **Check on the simulation:** in every cell, the simulated variance of the sample mean matched the exact formula within sampling error.\n\n**Choices and limitations:**\n- I took the bootstrap's \"0.95 empirical quantile\" to be the 380th of the 399 sorted values, the usual convention. Other quantile definitions would give slightly different critical values.\n- With 5 clusters there are only 32 possible bootstrap weight patterns, so the bootstrap distribution is very coarse. I recorded this under `data_issues`.\n- WCB under-covers at G = 5 even though its intervals are wider on average than CR1's. The report offers an explanation for this, but I did not test it, and `claims.json` marks it \"not_evaluated\".\n- The design covers only normal data, equal cluster variances, one pattern of cluster sizes, a plain mean with no regressors, and Rademacher bootstrap weights. The report lists what it leaves out.\n\nFiles are in `out/`:\n- results.json\n- table1.csv\n- figure1.png\n- report.md\n- claims.json\n- analysis.py\n- cell_diagnostics.csv (extra: per-cell checks and Monte Carlo errors of the variance ratios)"}],"container":null,"stop_reason":"end_turn","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":239,"cache_read_input_tokens":21078,"output_tokens":1083,"output_tokens_details":{"thinking_tokens":8},"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":239},"inference_geo":"global","iterations":[],"speed":"standard","fallback_credit":null},"input_transformations":[],"diagnostics":null,"context_management":null},"apiBlockIndex":1,"requestId":"req_011CgXtLrisRmnaWoDU4XnZk","requestRef":"a0053373-d30d-4de6-9c6a-5943ce260527","requestedModel":"claude-opus-5-5","type":"assistant","uuid":"4584bcba-548d-423e-9615-0bea718a8076","timestamp":"2026-10-30T05:41:10.400Z","effort":"medium","perTurnEffort":"medium","userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RT0022","sessionId":"4a686388-81c9-4843-bd6d-090bcf8e9da0","version":"2.1.314","gitBranch":"HEAD"}61{"type":"last-prompt","lastPrompt":"Begin","leafUuid":"4584bcba-548d-423e-9615-0bea718a8076","sessionId":"4a686388-81c9-4843-bd6d-090bcf8e9da0"}62{"type":"cost-state","sessionId":"4a686388-81c9-4843-bd6d-090bcf8e9da0","totalCostUSD":0.3574258,"totalAPIDuration":108594,"totalAPIDurationWithoutRetries":108587,"totalToolDuration":8550,"totalLinesAdded":0,"totalLinesRemoved":0,"totalDuration":117337,"startTime":1793338753071,"modelUsage":{"claude-opus-5-5":{"inputTokens":20,"outputTokens":11972,"thinkingTokens":1471,"cacheReadInputTokens":94204,"cacheCreationInputTokens":19813,"cacheCreationEphemeral5mInputTokens":19813,"cacheCreationEphemeral1hInputTokens":0,"webSearchRequests":0,"costUSD":0.3574258}},"hasUnknownModelCost":false}63