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A mini-project on simulated firm data\n\n## Scientific question\nA one-year training programme offered firms a variable number of training hours per employee. Using the simulated firm-level data provided, estimate the effect of one additional training hour per employee on productivity growth in the following year. The estimand is the causal effect of `training_hours` on `productivity_growth` (percentage points per hour) under a selection-on-observables assumption: conditional on the pre-programme information in the file, training hours are as good as randomly assigned, and the effect is linear and homogeneous. Report both the unadjusted association (the required baseline) and the estimate that targets the estimand, with uncertainty. The chain required is: question → data audit → baseline → justified method → results → uncertainty → failure discussion → one figure and one table → short report.\n\n## Inputs\n- `data/firms.csv` — simulated data (generated for this exercise; it is not observed data). One row per firm. Columns: `firm_id` (integer), `skill_index` (standardised index of the workforce's baseline skill, measured in the year *before* the programme), `training_hours` (hours of training per employee received during the programme year), `productivity_growth` (percent change in labour productivity in the year *after* the programme year). Rows are in random order.\n\n## Information-availability rules\n- Use only `data/firms.csv`. No other variables exist; nothing needs to be imputed.\n- The \"unadjusted\" model is the OLS regression of `productivity_growth` on `training_hours` with an intercept only.\n\n## Required outputs\n- `out/results.json` with the keys:\n\n| key | type | unit | meaning |\n|---|---|---|---|\n| `naive_estimate` | number | pp per hour | OLS slope of the unadjusted model (baseline) |\n| `naive_ci_low`, `naive_ci_high` | number | pp per hour | 95% confidence interval for the unadjusted slope |\n| `baseline_metric` | number | percentage points | RMSE of the unadjusted model's residuals, sqrt(mean(e²)) |\n| `model_metric` | number | percentage points | RMSE of the residuals of the model that targets the estimand |\n| `effect_estimate` | number | pp per hour | estimate of the estimand (coefficient on `training_hours` in the model you justify) |\n| `effect_se` | number | pp per hour | its standard error (conventional or heteroskedasticity-robust; state which) |\n| `effect_ci_low`, `effect_ci_high` | number | pp per hour | 95% confidence interval for the effect |\n| `skill_index_coefficient` | number or null | pp per index unit | coefficient on `skill_index` in the model that targets the estimand (null if that model does not include it) |\n| `conclusion` | string | — | one of the codes below, based on `effect_ci_low`/`effect_ci_high` |\n| `task_id` | string | copied from this file |\n| `feasibility` | string | `complete` / `partial` / `infeasible` |\n| `feasibility_reason` | string or null | why, if not complete |\n| `data_issues` | array of {\"issue\",\"handling\"} | every data problem detected and how it was handled (empty list allowed) |\n| `n_input_rows` | object {file: integer} | rows read per input file (file name as key, header row excluded) |\n| `n_analysed` | integer or null | observations actually used in the main estimate |\n| `random_seed` | integer or null | seed used, if any |\n\nThe `conclusion` code must be one of: `positive_effect` (the 95% interval for the effect estimand lies above zero), `negative_effect` (lies below zero), `no_detectable_effect` (the interval / test does not reject zero at the 5% level), `premise_invalid` (the data provided do not allow the stated estimand to be computed — in that case set `feasibility` to `infeasible`, explain in `feasibility_reason`, and set every inestimable key to `null`; never substitute an invented or proxy estimate).\n\n- `out/figure1.png` — scatter of `productivity_growth` against `training_hours` with the unadjusted fitted line and the fitted line of the model that targets the estimand (evaluated at the mean of any other regressor), produced by `out/analysis.py` with matplotlib.\n- `out/table1.csv` — one row per model with columns `model`, `estimate`, `se`, `ci_low`, `ci_high` (header + at least two data rows), produced by `out/analysis.py`.\n- `out/report.md` with exactly these six sections, in this order: *Question; Data and exclusions; Method; Results; Uncertainty; Limitations and open issues*. The last section must contain a genuine failure discussion: what would invalidate the result, what was not checked, and what the main threats are.\n- `out/analysis.py` — replication entry point: `~/wp29/venv/bin/python out/analysis.py`, run from the workspace root, must regenerate `out/results.json`, `out/figure1.png` and `out/table1.csv` from `data/` only, deterministically (fix any seed; use the matplotlib `Agg` backend).\n- `out/claims.json` — list of the central claims of the report: `[{\"id\": \"C1\", \"text\": \"...\", \"results_keys\": [\"...\"], \"status\": \"supported\"|\"unsupported\"|\"not_evaluated\", \"caveat\": \"...\"}]`. Every claim must reference existing keys of `out/results.json`; a claim must only be marked `supported` if the reported numbers actually support it.\n\n## Constraints\n- No network access. Python: `~/wp29/venv/bin/python` (numpy, pandas, scipy, statsmodels, matplotlib available). Do not modify `data/`.\n- Write all deliverables under `out/`. This task is designed to be completed in well under 20 minutes of work; prefer a short, correct chain over an elaborate one.\ntotal 232\ndrwxr-xr-x 11 simon-pierreboucher staff 352 oct. 30 00:08 .\ndrwxr-xr-x 7 simon-pierreboucher staff 224 oct. 30 00:07 ..\n-rw-r--r-- 1 simon-pierreboucher staff 4810 oct. 30 00:07 analysis.py\n-rw-r--r-- 1 simon-pierreboucher staff 1627 oct. 30 00:08 claims.json\n-rw-r--r-- 1 simon-pierreboucher staff 142 oct. 30 00:07 exclusions.csv\n-rw-r--r-- 1 simon-pierreboucher staff 85051 oct. 30 00:07 figure1.png\n-rw-r--r-- 1 simon-pierreboucher staff 5207 oct. 30 00:08 report.md\n-rw-r--r-- 1 simon-pierreboucher staff 1304 oct. 30 00:07 results.json\ndrwxr-xr-x 2 simon-pierreboucher staff 64 oct. 30 00:08 review\ndrwxr-xr-x 8 simon-pierreboucher staff 256 oct. 30 00:08 state\n-rw-r--r-- 1 simon-pierreboucher staff 733 oct. 30 00:07 table1.csv\n{\n \"task_id\": \"F6-conf-04\",\n \"feasibility\": \"complete\",\n \"feasibility_reason\": null,\n \"data_issues\": [\n {\n \"issue\": \"12 rows have negative training_hours (min -7.585), physically impossible\",\n \"handling\": \"kept in main analysis (smooth continuous tail of simulated hours-skill relation; dropping would truncate on the treatment); sensitivity analysis excluding them reported in table1.csv\"\n }\n ],\n \"n_input_rows\": {\n \"firms.csv\": 1200\n },\n \"n_analysed\": 1200,\n \"random_seed\": null,\n \"naive_estimate\": -0.13518960504279864,\n \"naive_ci_low\": -0.16242716225177342,\n \"naive_ci_high\": -0.10795204783382387,\n \"baseline_metric\": 3.860935650765891,\n \"model_metric\": 1.9773229272347406,\n \"effect_estimate\": 0.25222449623309495,\n \"effect_se\": 0.00947885217664965,\n \"effect_se_type\": \"HC1 heteroskedasticity-robust\",\n \"effect_ci_low\": 0.23364628735208254,\n \"effect_ci_high\": 0.27080270511410737,\n \"skill_index_coefficient\": -4.630436409471269,\n \"skill_index_se\": 0.0780892771606384,\n \"corr_skill_hours\": 0.6935300645961768,\n \"sens_excl_negative_hours_estimate\": 0.25353820594501253,\n \"sens_excl_negative_hours_n\": 1188,\n \"sens_skill_quadratic_estimate\": 0.25225435228148574,\n \"sens_skill_quadratic_sq_coef_pvalue\": 0.8131468023741133,\n \"conclusion\": \"positive_effect\"\n}[\n {\"id\":\"C1\",\"text\":\"The unadjusted OLS slope of productivity growth on training hours is negative, about -0.135 pp/hour with 95% CI excluding zero.\",\"results_keys\":[\"naive_estimate\",\"naive_ci_low\",\"naive_ci_high\"],\"status\":\"supported\",\"caveat\":\"Descriptive association only; confounded by skill.\"},\n {\"id\":\"C2\",\"text\":\"Adjusting for pre-programme skill_index, one extra training hour per employee raises next-year productivity growth by about 0.25 pp (95% CI 0.234-0.271), conclusion positive_effect.\",\"results_keys\":[\"effect_estimate\",\"effect_se\",\"effect_ci_low\",\"effect_ci_high\",\"conclusion\"],\"status\":\"supported\",\"caveat\":\"Causal only under selection on observables with skill_index as the sole confounder and a linear homogeneous effect.\"},\n {\"id\":\"C3\",\"text\":\"The sign reversal is explained by confounding: skill is positively correlated with hours and negatively related to growth.\",\"results_keys\":[\"skill_index_coefficient\",\"corr_skill_hours\",\"naive_estimate\",\"effect_estimate\"],\"status\":\"supported\",\"caveat\":\"Omitted-variable identity verified numerically in checks.md.\"},\n {\"id\":\"C4\",\"text\":\"Adding skill_index roughly halves residual RMSE (3.86 to 1.98 pp).\",\"results_keys\":[\"baseline_metric\",\"model_metric\"],\"status\":\"supported\",\"caveat\":\"In-sample fit.\"},\n {\"id\":\"C5\",\"text\":\"The estimate is robust to excluding the 12 negative-hours rows and to a quadratic skill term.\",\"results_keys\":[\"sens_excl_negative_hours_estimate\",\"sens_excl_negative_hours_n\",\"sens_skill_quadratic_estimate\",\"sens_skill_quadratic_sq_coef_pvalue\",\"data_issues\"],\"status\":\"supported\",\"caveat\":\"Does not address unobserved confounding.\"}\n]","is_error":false}]},"uuid":"322275f6-e666-47df-9550-70c3ebf2453e","timestamp":"2026-10-30T04:08:51.225Z","toolUseResult":{"stdout":"# F6-conf-04 — Does workforce training raise productivity growth? A mini-project on simulated firm data\n\n## Scientific question\nA one-year training programme offered firms a variable number of training hours per employee. Using the simulated firm-level data provided, estimate the effect of one additional training hour per employee on productivity growth in the following year. The estimand is the causal effect of `training_hours` on `productivity_growth` (percentage points per hour) under a selection-on-observables assumption: conditional on the pre-programme information in the file, training hours are as good as randomly assigned, and the effect is linear and homogeneous. Report both the unadjusted association (the required baseline) and the estimate that targets the estimand, with uncertainty. The chain required is: question → data audit → baseline → justified method → results → uncertainty → failure discussion → one figure and one table → short report.\n\n## Inputs\n- `data/firms.csv` — simulated data (generated for this exercise; it is not observed data). One row per firm. Columns: `firm_id` (integer), `skill_index` (standardised index of the workforce's baseline skill, measured in the year *before* the programme), `training_hours` (hours of training per employee received during the programme year), `productivity_growth` (percent change in labour productivity in the year *after* the programme year). Rows are in random order.\n\n## Information-availability rules\n- Use only `data/firms.csv`. No other variables exist; nothing needs to be imputed.\n- The \"unadjusted\" model is the OLS regression of `productivity_growth` on `training_hours` with an intercept only.\n\n## Required outputs\n- `out/results.json` with the keys:\n\n| key | type | unit | meaning |\n|---|---|---|---|\n| `naive_estimate` | number | pp per hour | OLS slope of the unadjusted model (baseline) |\n| `naive_ci_low`, `naive_ci_high` | number | pp per hour | 95% confidence interval for the unadjusted slope |\n| `baseline_metric` | number | percentage points | RMSE of the unadjusted model's residuals, sqrt(mean(e²)) |\n| `model_metric` | number | percentage points | RMSE of the residuals of the model that targets the estimand |\n| `effect_estimate` | number | pp per hour | estimate of the estimand (coefficient on `training_hours` in the model you justify) |\n| `effect_se` | number | pp per hour | its standard error (conventional or heteroskedasticity-robust; state which) |\n| `effect_ci_low`, `effect_ci_high` | number | pp per hour | 95% confidence interval for the effect |\n| `skill_index_coefficient` | number or null | pp per index unit | coefficient on `skill_index` in the model that targets the estimand (null if that model does not include it) |\n| `conclusion` | string | — | one of the codes below, based on `effect_ci_low`/`effect_ci_high` |\n| `task_id` | string | copied from this file |\n| `feasibility` | string | `complete` / `partial` / `infeasible` |\n| `feasibility_reason` | string or null | why, if not complete |\n| `data_issues` | array of {\"issue\",\"handling\"} | every data problem detected and how it was handled (empty list allowed) |\n| `n_input_rows` | object {file: integer} | rows read per input file (file name as key, header row excluded) |\n| `n_analysed` | integer or null | observations actually used in the main estimate |\n| `random_seed` | integer or null | seed used, if any |\n\nThe `conclusion` code must be one of: `positive_effect` (the 95% interval for the effect estimand lies above zero), `negative_effect` (lies below zero), `no_detectable_effect` (the interval / test does not reject zero at the 5% level), `premise_invalid` (the data provided do not allow the stated estimand to be computed — in that case set `feasibility` to `infeasible`, explain in `feasibility_reason`, and set every inestimable key to `null`; never substitute an invented or proxy estimate).\n\n- `out/figure1.png` — scatter of `productivity_growth` against `training_hours` with the unadjusted fitted line and the fitted line of the model that targets the estimand (evaluated at the mean of any other regressor), produced by `out/analysis.py` with matplotlib.\n- `out/table1.csv` — one row per model with columns `model`, `estimate`, `se`, `ci_low`, `ci_high` (header + at least two data rows), produced by `out/analysis.py`.\n- `out/report.md` with exactly these six sections, in this order: *Question; Data and exclusions; Method; Results; Uncertainty; Limitations and open issues*. The last section must contain a genuine failure discussion: what would invalidate the result, what was not checked, and what the main threats are.\n- `out/analysis.py` — replication entry point: `~/wp29/venv/bin/python out/analysis.py`, run from the workspace root, must regenerate `out/results.json`, `out/figure1.png` and `out/table1.csv` from `data/` only, deterministically (fix any seed; use the matplotlib `Agg` backend).\n- `out/claims.json` — list of the central claims of the report: `[{\"id\": \"C1\", \"text\": \"...\", \"results_keys\": [\"...\"], \"status\": \"supported\"|\"unsupported\"|\"not_evaluated\", \"caveat\": \"...\"}]`. Every claim must reference existing keys of `out/results.json`; a claim must only be marked `supported` if the reported numbers actually support it.\n\n## Constraints\n- No network access. Python: `~/wp29/venv/bin/python` (numpy, pandas, scipy, statsmodels, matplotlib available). Do not modify `data/`.\n- Write all deliverables under `out/`. This task is designed to be completed in well under 20 minutes of work; prefer a short, correct chain over an elaborate one.\ntotal 232\ndrwxr-xr-x 11 simon-pierreboucher staff 352 oct. 30 00:08 .\ndrwxr-xr-x 7 simon-pierreboucher staff 224 oct. 30 00:07 ..\n-rw-r--r-- 1 simon-pierreboucher staff 4810 oct. 30 00:07 analysis.py\n-rw-r--r-- 1 simon-pierreboucher staff 1627 oct. 30 00:08 claims.json\n-rw-r--r-- 1 simon-pierreboucher staff 142 oct. 30 00:07 exclusions.csv\n-rw-r--r-- 1 simon-pierreboucher staff 85051 oct. 30 00:07 figure1.png\n-rw-r--r-- 1 simon-pierreboucher staff 5207 oct. 30 00:08 report.md\n-rw-r--r-- 1 simon-pierreboucher staff 1304 oct. 30 00:07 results.json\ndrwxr-xr-x 2 simon-pierreboucher staff 64 oct. 30 00:08 review\ndrwxr-xr-x 8 simon-pierreboucher staff 256 oct. 30 00:08 state\n-rw-r--r-- 1 simon-pierreboucher staff 733 oct. 30 00:07 table1.csv\n{\n \"task_id\": \"F6-conf-04\",\n \"feasibility\": \"complete\",\n \"feasibility_reason\": null,\n \"data_issues\": [\n {\n \"issue\": \"12 rows have negative training_hours (min -7.585), physically impossible\",\n \"handling\": \"kept in main analysis (smooth continuous tail of simulated hours-skill relation; dropping would truncate on the treatment); sensitivity analysis excluding them reported in table1.csv\"\n }\n ],\n \"n_input_rows\": {\n \"firms.csv\": 1200\n },\n \"n_analysed\": 1200,\n \"random_seed\": null,\n \"naive_estimate\": -0.13518960504279864,\n \"naive_ci_low\": -0.16242716225177342,\n \"naive_ci_high\": -0.10795204783382387,\n \"baseline_metric\": 3.860935650765891,\n \"model_metric\": 1.9773229272347406,\n \"effect_estimate\": 0.25222449623309495,\n \"effect_se\": 0.00947885217664965,\n \"effect_se_type\": \"HC1 heteroskedasticity-robust\",\n \"effect_ci_low\": 0.23364628735208254,\n \"effect_ci_high\": 0.27080270511410737,\n \"skill_index_coefficient\": -4.630436409471269,\n \"skill_index_se\": 0.0780892771606384,\n \"corr_skill_hours\": 0.6935300645961768,\n \"sens_excl_negative_hours_estimate\": 0.25353820594501253,\n \"sens_excl_negative_hours_n\": 1188,\n \"sens_skill_quadratic_estimate\": 0.25225435228148574,\n \"sens_skill_quadratic_sq_coef_pvalue\": 0.8131468023741133,\n \"conclusion\": \"positive_effect\"\n}[\n {\"id\":\"C1\",\"text\":\"The unadjusted OLS slope of productivity growth on training hours is negative, about -0.135 pp/hour with 95% CI excluding zero.\",\"results_keys\":[\"naive_estimate\",\"naive_ci_low\",\"naive_ci_high\"],\"status\":\"supported\",\"caveat\":\"Descriptive association only; confounded by skill.\"},\n {\"id\":\"C2\",\"text\":\"Adjusting for pre-programme skill_index, one extra training hour per employee raises next-year productivity growth by about 0.25 pp (95% CI 0.234-0.271), conclusion positive_effect.\",\"results_keys\":[\"effect_estimate\",\"effect_se\",\"effect_ci_low\",\"effect_ci_high\",\"conclusion\"],\"status\":\"supported\",\"caveat\":\"Causal only under selection on observables with skill_index as the sole confounder and a linear homogeneous effect.\"},\n {\"id\":\"C3\",\"text\":\"The sign reversal is explained by confounding: skill is positively correlated with hours and negatively related to growth.\",\"results_keys\":[\"skill_index_coefficient\",\"corr_skill_hours\",\"naive_estimate\",\"effect_estimate\"],\"status\":\"supported\",\"caveat\":\"Omitted-variable identity verified numerically in checks.md.\"},\n {\"id\":\"C4\",\"text\":\"Adding skill_index roughly halves residual RMSE (3.86 to 1.98 pp).\",\"results_keys\":[\"baseline_metric\",\"model_metric\"],\"status\":\"supported\",\"caveat\":\"In-sample fit.\"},\n {\"id\":\"C5\",\"text\":\"The estimate is robust to excluding the 12 negative-hours rows and to a quadratic skill term.\",\"results_keys\":[\"sens_excl_negative_hours_estimate\",\"sens_excl_negative_hours_n\",\"sens_skill_quadratic_estimate\",\"sens_skill_quadratic_sq_coef_pvalue\",\"data_issues\"],\"status\":\"supported\",\"caveat\":\"Does not address unobserved confounding.\"}\n]","stderr":"","interrupted":false,"isImage":false,"noOutputExpected":false},"sandboxed":false,"sourceToolAssistantUUID":"3911d154-aebf-439f-b6d4-fd57691fa61e","permissionDecision":{"decision":"accept","source":"config","reasonType":"rule","ruleSource":"cliArg"},"userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0019","sessionId":"e64ce0b2-649e-420f-9a9b-b5c5e2362545","version":"2.1.314","gitBranch":"HEAD"}15{"type":"api-request-blob","sessionId":"e64ce0b2-649e-420f-9a9b-b5c5e2362545","hash":"da9de77475878f77e59a9b71168c94bb80ca37905bce618c741646e528e1a8fe","message":{"role":"assistant","content":[{"type":"thinking","thinking":"","signature":"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"},{"type":"tool_use","id":"toolu_012M3PV3w2bQQBEEoEAuM6qZ","name":"Bash","input":{"command":"cat TASK.md; 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Run from workspace root:\n ~/wp29/venv/bin/python out/analysis.py\nReads data/firms.csv only; writes out/results.json, out/table1.csv, out/figure1.png, out/exclusions.csv.\nNo randomness is used (random_seed = null).\"\"\"\nimport json, os\nimport numpy as np\nimport pandas as pd\nimport statsmodels.formula.api as smf\nimport matplotlib\nmatplotlib.use(\"Agg\")\nimport matplotlib.pyplot as plt\n\nOUT = \"out\"\nos.makedirs(OUT, exist_ok=True)\ndf = pd.read_csv(\"data/firms.csv\")\nn_raw = len(df)\n\n# ---- audit ----\nissues = []\nn_neg = int((df.training_hours < 0).sum())\nif n_neg:\n issues.append({\"issue\": f\"{n_neg} rows have negative training_hours (min {df.training_hours.min():.3f}), physically impossible\",\n \"handling\": \"kept in main analysis (smooth continuous tail of simulated hours-skill relation; dropping would truncate on the treatment); sensitivity analysis excluding them reported in table1.csv\"})\nassert df.isna().sum().sum() == 0 and not df.firm_id.duplicated().any()\n\n# exclusion ledger: no rows excluded in main analysis\npd.DataFrame([{\"step\": 1, \"rule\": \"drop rows with missing values or duplicate firm_id\", \"n_before\": n_raw,\n \"n_after\": n_raw, \"n_removed\": 0, \"rationale\": \"none found; all rows retained\"}]\n ).to_csv(f\"{OUT}/exclusions.csv\", index=False)\n\ndef fit(formula, data):\n return smf.ols(formula, data=data).fit(cov_type=\"HC1\")\n\ndef rmse(m):\n return float(np.sqrt(np.mean(m.resid ** 2)))\n\nnaive = fit(\"productivity_growth ~ training_hours\", df)\nmain = fit(\"productivity_growth ~ training_hours + skill_index\", df)\nsens_pos = fit(\"productivity_growth ~ training_hours + skill_index\", df[df.training_hours >= 0])\nsens_quad = fit(\"productivity_growth ~ training_hours + skill_index + I(skill_index**2)\", df)\n# conventional-SE version of main model for comparison\nmain_conv = smf.ols(\"productivity_growth ~ training_hours + skill_index\", data=df).fit()\n\ndef row(name, m):\n ci = m.conf_int().loc[\"training_hours\"]\n return {\"model\": name, \"estimate\": float(m.params[\"training_hours\"]), \"se\": float(m.bse[\"training_hours\"]),\n \"ci_low\": float(ci[0]), \"ci_high\": float(ci[1]), \"n\": int(m.nobs), \"rmse\": rmse(m)}\n\nrows = [row(\"unadjusted: y ~ hours (HC1)\", naive),\n row(\"adjusted (main): y ~ hours + skill (HC1)\", main),\n row(\"adjusted, conventional SE\", main_conv),\n row(\"sensitivity: main excl. negative hours (HC1)\", sens_pos),\n row(\"sensitivity: + skill^2 (HC1)\", sens_quad)]\npd.DataFrame(rows).to_csv(f\"{OUT}/table1.csv\", index=False)\n\nr_n, r_m = rows[0], rows[1]\nlo, hi = r_m[\"ci_low\"], r_m[\"ci_high\"]\nconclusion = \"positive_effect\" if lo > 0 else (\"negative_effect\" if hi < 0 else \"no_detectable_effect\")\n\nresults = {\n \"task_id\": \"F6-conf-04\",\n \"feasibility\": \"complete\",\n \"feasibility_reason\": None,\n \"data_issues\": issues,\n \"n_input_rows\": {\"firms.csv\": n_raw},\n \"n_analysed\": int(main.nobs),\n \"random_seed\": None,\n \"naive_estimate\": r_n[\"estimate\"],\n \"naive_ci_low\": r_n[\"ci_low\"],\n \"naive_ci_high\": r_n[\"ci_high\"],\n \"baseline_metric\": r_n[\"rmse\"],\n \"model_metric\": r_m[\"rmse\"],\n \"effect_estimate\": r_m[\"estimate\"],\n \"effect_se\": r_m[\"se\"],\n \"effect_se_type\": \"HC1 heteroskedasticity-robust\",\n \"effect_ci_low\": lo,\n \"effect_ci_high\": hi,\n \"skill_index_coefficient\": float(main.params[\"skill_index\"]),\n \"skill_index_se\": float(main.bse[\"skill_index\"]),\n \"corr_skill_hours\": float(df.skill_index.corr(df.training_hours)),\n \"sens_excl_negative_hours_estimate\": rows[3][\"estimate\"],\n \"sens_excl_negative_hours_n\": rows[3][\"n\"],\n \"sens_skill_quadratic_estimate\": rows[4][\"estimate\"],\n \"sens_skill_quadratic_sq_coef_pvalue\": float(sens_quad.pvalues[\"I(skill_index ** 2)\"]),\n \"conclusion\": conclusion,\n}\nwith open(f\"{OUT}/results.json\", \"w\") as f:\n json.dump(results, f, indent=2)\n\n# ---- figure ----\nfig, ax = plt.subplots(figsize=(7, 5))\nax.scatter(df.training_hours, df.productivity_growth, s=6, alpha=0.4, color=\"grey\", label=\"firms\")\nx = np.linspace(df.training_hours.min(), df.training_hours.max(), 100)\nax.plot(x, naive.params[\"Intercept\"] + naive.params[\"training_hours\"] * x, color=\"C3\",\n label=f\"unadjusted (slope {r_n['estimate']:.3f})\")\nax.plot(x, main.params[\"Intercept\"] + main.params[\"training_hours\"] * x + main.params[\"skill_index\"] * df.skill_index.mean(),\n color=\"C0\", label=f\"adjusted for skill, at mean skill (slope {r_m['estimate']:.3f})\")\nax.set_xlabel(\"training hours per employee\")\nax.set_ylabel(\"productivity growth next year (%)\")\nax.legend()\nax.set_title(\"Productivity growth vs training hours (simulated)\")\nfig.tight_layout()\nfig.savefig(f\"{OUT}/figure1.png\", dpi=120)\nprint(json.dumps(results, indent=2))\nprint(pd.DataFrame(rows).to_string())\n# F6-conf-04 — Effect of workforce training hours on productivity growth (simulated data)\n\n## Question\nWhat is the causal effect of one extra hour of training per employee on productivity growth (in percentage points) in the year after the programme? We assume selection on observables: once we condition on the pre-programme `skill_index`, training hours are as good as randomly assigned. We also assume the effect is linear and the same for every firm. The data are simulated.\n\n## Data and exclusions\n`data/firms.csv` has 1200 rows (`n_input_rows`) and 4 columns. There are no missing values and no duplicate firms (see `out/state/data_audit.md`). One problem was found (`data_issues`): 12 firms report negative training hours (minimum −7.585 h), which cannot happen in reality. All 12 are low-skill firms and sit in the smooth lower tail of the hours–skill relationship, so they look like an artefact of the simulation rather than isolated recording errors. I kept them in the main model: dropping them would mean selecting the sample on the treatment variable. A sensitivity run without them is reported below. No rows were excluded (`out/exclusions.csv`), so `n_analysed` = 1200. No randomness was used (`random_seed` = null).\n\n## Method\n- **Baseline (unadjusted):** OLS of `productivity_growth` on `training_hours` with an intercept.\n- **Main model:** OLS of `productivity_growth` on `training_hours` and `skill_index` with an intercept. `skill_index` was measured in the year before the programme, so it is a pre-treatment confounder. Controlling for it cannot introduce mediator or collider bias. It is the only covariate available, so under the stated assumption this regression identifies the estimand.\n- **Standard errors:** HC1 heteroskedasticity-robust (`effect_se_type`). 95% confidence intervals use the normal approximation.\n- **Fit metric:** in-sample residual RMSE, sqrt(mean(e²)).\n- **Checks:** a model adding skill², and the main model without the negative-hours rows.\n- **Outputs:** `out/table1.csv` (all models) and `out/figure1.png` (scatter with both fitted lines; the adjusted line is drawn at mean skill).\n\n## Results\n- **Unadjusted slope:** `naive_estimate` = −0.135 pp/hour, 95% CI [−0.162, −0.108] (`naive_ci_low`, `naive_ci_high`). RMSE `baseline_metric` = 3.86 pp.\n- **Adjusted effect:** `effect_estimate` = **0.252 pp/hour**, SE 0.0095 (`effect_se`), 95% CI [0.234, 0.271] (`effect_ci_low`, `effect_ci_high`). RMSE `model_metric` = 1.98 pp.\n- **Skill coefficient:** `skill_index_coefficient` = −4.63 pp per index unit.\n- **Why the sign flips:** higher-skill firms train more (`corr_skill_hours` = 0.69) and also grow less, so the unadjusted slope mixes these two relationships. The standard omitted-variable formula (effect + skill coefficient × slope of skill on hours) reproduces the naive slope exactly (`out/state/checks.md`).\n- **Conclusion code:** `conclusion` = `positive_effect`. Under the assumptions, one extra training hour per employee raises next-year productivity growth by about 0.25 pp.\n\n## Uncertainty\n- **Sampling uncertainty is small:** the robust 95% CI is [0.234, 0.271]. Conventional SEs are almost the same (0.0096, `out/table1.csv`).\n- **Sensitivity checks barely move the estimate:**\n - without the 12 negative-hours rows: `sens_excl_negative_hours_estimate` = 0.2535 (n = `sens_excl_negative_hours_n` = 1188);\n - adding skill²: `sens_skill_quadratic_estimate` = 0.2523. The quadratic term is not significant (`sens_skill_quadratic_sq_coef_pvalue` = 0.81).\n- **What the CI does not cover:** it reflects sampling noise only. It does not account for the identification assumptions failing.\n\n## Limitations and open issues\n- **What would invalidate the result:** the main threat is unobserved confounding. The causal reading rests entirely on `skill_index` being the only common cause of training and growth. If managers chose training based on anything else (expected growth, management quality, demand shocks), the estimate is biased. The direction of the bias is unknown, and its size could be large given how strongly the naive and adjusted estimates differ. The data cannot test this assumption.\n- **Measurement error in skill:** `skill_index` is an index. If it measures true skill with error, the adjustment is incomplete. Given the strong confounding here, that would bias the training coefficient back toward the (negative) naive value.\n- **Not checked:**\n - effects that differ across firms or depend on skill (no hours × skill interaction model);\n - non-linear effects of hours (e.g. diminishing returns);\n - overlap and common support at the extremes of skill. Hours and skill are correlated at 0.69, so high-skill/low-hours and low-skill/high-hours combinations are rare and those estimates lean on the linear form;\n - spillovers between firms.\n- **Data validity:** the 12 negative hours values show that the generating process is not a fully realistic hours variable. I kept them, and the result does not depend on that choice.\n- **External validity:** the data are simulated. Results apply only to this exercise.\n- The RMSE metrics are in-sample. The task asked for no out-of-sample validation and none was done.\nmodel,estimate,se,ci_low,ci_high,n,rmse\nunadjusted: y ~ hours (HC1),-0.13518960504279864,0.013896968221774055,-0.16242716225177342,-0.10795204783382387,1200,3.860935650765891\nadjusted (main): y ~ hours + skill (HC1),0.25222449623309495,0.00947885217664965,0.23364628735208254,0.27080270511410737,1200,1.9773229272347406\n\"adjusted, conventional SE\",0.25222449623309495,0.009627270798215685,0.2333362934392722,0.2711126990269177,1200,1.9773229272347406\nsensitivity: main excl. negative hours (HC1),0.25353820594501253,0.009812212326345536,0.2343066231767153,0.27276978871330976,1188,1.9758571828823268\nsensitivity: + skill^2 (HC1),0.25225435228148574,0.009488048505738672,0.23365811892666888,0.2708505856363026,1200,1.9772836921377606\nstep,rule,n_before,n_after,n_removed,rationale\n1,drop rows with missing values or duplicate firm_id,1200,1200,0,none found; all rows retained\nchecks.md\ncontract.md\ndata_audit.md\ngate_receipts\nlog.md\nphases.json","is_error":false}]},"uuid":"d09f83dd-5c25-41d6-90fc-370035ab974d","timestamp":"2026-10-30T04:08:53.269Z","toolUseResult":{"stdout":"\"\"\"Replication entry point for F6-conf-04. Run from workspace root:\n ~/wp29/venv/bin/python out/analysis.py\nReads data/firms.csv only; writes out/results.json, out/table1.csv, out/figure1.png, out/exclusions.csv.\nNo randomness is used (random_seed = null).\"\"\"\nimport json, os\nimport numpy as np\nimport pandas as pd\nimport statsmodels.formula.api as smf\nimport matplotlib\nmatplotlib.use(\"Agg\")\nimport matplotlib.pyplot as plt\n\nOUT = \"out\"\nos.makedirs(OUT, exist_ok=True)\ndf = pd.read_csv(\"data/firms.csv\")\nn_raw = len(df)\n\n# ---- audit ----\nissues = []\nn_neg = int((df.training_hours < 0).sum())\nif n_neg:\n issues.append({\"issue\": f\"{n_neg} rows have negative training_hours (min {df.training_hours.min():.3f}), physically impossible\",\n \"handling\": \"kept in main analysis (smooth continuous tail of simulated hours-skill relation; dropping would truncate on the treatment); sensitivity analysis excluding them reported in table1.csv\"})\nassert df.isna().sum().sum() == 0 and not df.firm_id.duplicated().any()\n\n# exclusion ledger: no rows excluded in main analysis\npd.DataFrame([{\"step\": 1, \"rule\": \"drop rows with missing values or duplicate firm_id\", \"n_before\": n_raw,\n \"n_after\": n_raw, \"n_removed\": 0, \"rationale\": \"none found; all rows retained\"}]\n ).to_csv(f\"{OUT}/exclusions.csv\", index=False)\n\ndef fit(formula, data):\n return smf.ols(formula, data=data).fit(cov_type=\"HC1\")\n\ndef rmse(m):\n return float(np.sqrt(np.mean(m.resid ** 2)))\n\nnaive = fit(\"productivity_growth ~ training_hours\", df)\nmain = fit(\"productivity_growth ~ training_hours + skill_index\", df)\nsens_pos = fit(\"productivity_growth ~ training_hours + skill_index\", df[df.training_hours >= 0])\nsens_quad = fit(\"productivity_growth ~ training_hours + skill_index + I(skill_index**2)\", df)\n# conventional-SE version of main model for comparison\nmain_conv = smf.ols(\"productivity_growth ~ training_hours + skill_index\", data=df).fit()\n\ndef row(name, m):\n ci = m.conf_int().loc[\"training_hours\"]\n return {\"model\": name, \"estimate\": float(m.params[\"training_hours\"]), \"se\": float(m.bse[\"training_hours\"]),\n \"ci_low\": float(ci[0]), \"ci_high\": float(ci[1]), \"n\": int(m.nobs), \"rmse\": rmse(m)}\n\nrows = [row(\"unadjusted: y ~ hours (HC1)\", naive),\n row(\"adjusted (main): y ~ hours + skill (HC1)\", main),\n row(\"adjusted, conventional SE\", main_conv),\n row(\"sensitivity: main excl. negative hours (HC1)\", sens_pos),\n row(\"sensitivity: + skill^2 (HC1)\", sens_quad)]\npd.DataFrame(rows).to_csv(f\"{OUT}/table1.csv\", index=False)\n\nr_n, r_m = rows[0], rows[1]\nlo, hi = r_m[\"ci_low\"], r_m[\"ci_high\"]\nconclusion = \"positive_effect\" if lo > 0 else (\"negative_effect\" if hi < 0 else \"no_detectable_effect\")\n\nresults = {\n \"task_id\": \"F6-conf-04\",\n \"feasibility\": \"complete\",\n \"feasibility_reason\": None,\n \"data_issues\": issues,\n \"n_input_rows\": {\"firms.csv\": n_raw},\n \"n_analysed\": int(main.nobs),\n \"random_seed\": None,\n \"naive_estimate\": r_n[\"estimate\"],\n \"naive_ci_low\": r_n[\"ci_low\"],\n \"naive_ci_high\": r_n[\"ci_high\"],\n \"baseline_metric\": r_n[\"rmse\"],\n \"model_metric\": r_m[\"rmse\"],\n \"effect_estimate\": r_m[\"estimate\"],\n \"effect_se\": r_m[\"se\"],\n \"effect_se_type\": \"HC1 heteroskedasticity-robust\",\n \"effect_ci_low\": lo,\n \"effect_ci_high\": hi,\n \"skill_index_coefficient\": float(main.params[\"skill_index\"]),\n \"skill_index_se\": float(main.bse[\"skill_index\"]),\n \"corr_skill_hours\": float(df.skill_index.corr(df.training_hours)),\n \"sens_excl_negative_hours_estimate\": rows[3][\"estimate\"],\n \"sens_excl_negative_hours_n\": rows[3][\"n\"],\n \"sens_skill_quadratic_estimate\": rows[4][\"estimate\"],\n \"sens_skill_quadratic_sq_coef_pvalue\": float(sens_quad.pvalues[\"I(skill_index ** 2)\"]),\n \"conclusion\": conclusion,\n}\nwith open(f\"{OUT}/results.json\", \"w\") as f:\n json.dump(results, f, indent=2)\n\n# ---- figure ----\nfig, ax = plt.subplots(figsize=(7, 5))\nax.scatter(df.training_hours, df.productivity_growth, s=6, alpha=0.4, color=\"grey\", label=\"firms\")\nx = np.linspace(df.training_hours.min(), df.training_hours.max(), 100)\nax.plot(x, naive.params[\"Intercept\"] + naive.params[\"training_hours\"] * x, color=\"C3\",\n label=f\"unadjusted (slope {r_n['estimate']:.3f})\")\nax.plot(x, main.params[\"Intercept\"] + main.params[\"training_hours\"] * x + main.params[\"skill_index\"] * df.skill_index.mean(),\n color=\"C0\", label=f\"adjusted for skill, at mean skill (slope {r_m['estimate']:.3f})\")\nax.set_xlabel(\"training hours per employee\")\nax.set_ylabel(\"productivity growth next year (%)\")\nax.legend()\nax.set_title(\"Productivity growth vs training hours (simulated)\")\nfig.tight_layout()\nfig.savefig(f\"{OUT}/figure1.png\", dpi=120)\nprint(json.dumps(results, indent=2))\nprint(pd.DataFrame(rows).to_string())\n# F6-conf-04 — Effect of workforce training hours on productivity growth (simulated data)\n\n## Question\nWhat is the causal effect of one extra hour of training per employee on productivity growth (in percentage points) in the year after the programme? We assume selection on observables: once we condition on the pre-programme `skill_index`, training hours are as good as randomly assigned. We also assume the effect is linear and the same for every firm. The data are simulated.\n\n## Data and exclusions\n`data/firms.csv` has 1200 rows (`n_input_rows`) and 4 columns. There are no missing values and no duplicate firms (see `out/state/data_audit.md`). One problem was found (`data_issues`): 12 firms report negative training hours (minimum −7.585 h), which cannot happen in reality. All 12 are low-skill firms and sit in the smooth lower tail of the hours–skill relationship, so they look like an artefact of the simulation rather than isolated recording errors. I kept them in the main model: dropping them would mean selecting the sample on the treatment variable. A sensitivity run without them is reported below. No rows were excluded (`out/exclusions.csv`), so `n_analysed` = 1200. No randomness was used (`random_seed` = null).\n\n## Method\n- **Baseline (unadjusted):** OLS of `productivity_growth` on `training_hours` with an intercept.\n- **Main model:** OLS of `productivity_growth` on `training_hours` and `skill_index` with an intercept. `skill_index` was measured in the year before the programme, so it is a pre-treatment confounder. Controlling for it cannot introduce mediator or collider bias. It is the only covariate available, so under the stated assumption this regression identifies the estimand.\n- **Standard errors:** HC1 heteroskedasticity-robust (`effect_se_type`). 95% confidence intervals use the normal approximation.\n- **Fit metric:** in-sample residual RMSE, sqrt(mean(e²)).\n- **Checks:** a model adding skill², and the main model without the negative-hours rows.\n- **Outputs:** `out/table1.csv` (all models) and `out/figure1.png` (scatter with both fitted lines; the adjusted line is drawn at mean skill).\n\n## Results\n- **Unadjusted slope:** `naive_estimate` = −0.135 pp/hour, 95% CI [−0.162, −0.108] (`naive_ci_low`, `naive_ci_high`). RMSE `baseline_metric` = 3.86 pp.\n- **Adjusted effect:** `effect_estimate` = **0.252 pp/hour**, SE 0.0095 (`effect_se`), 95% CI [0.234, 0.271] (`effect_ci_low`, `effect_ci_high`). RMSE `model_metric` = 1.98 pp.\n- **Skill coefficient:** `skill_index_coefficient` = −4.63 pp per index unit.\n- **Why the sign flips:** higher-skill firms train more (`corr_skill_hours` = 0.69) and also grow less, so the unadjusted slope mixes these two relationships. The standard omitted-variable formula (effect + skill coefficient × slope of skill on hours) reproduces the naive slope exactly (`out/state/checks.md`).\n- **Conclusion code:** `conclusion` = `positive_effect`. Under the assumptions, one extra training hour per employee raises next-year productivity growth by about 0.25 pp.\n\n## Uncertainty\n- **Sampling uncertainty is small:** the robust 95% CI is [0.234, 0.271]. Conventional SEs are almost the same (0.0096, `out/table1.csv`).\n- **Sensitivity checks barely move the estimate:**\n - without the 12 negative-hours rows: `sens_excl_negative_hours_estimate` = 0.2535 (n = `sens_excl_negative_hours_n` = 1188);\n - adding skill²: `sens_skill_quadratic_estimate` = 0.2523. The quadratic term is not significant (`sens_skill_quadratic_sq_coef_pvalue` = 0.81).\n- **What the CI does not cover:** it reflects sampling noise only. It does not account for the identification assumptions failing.\n\n## Limitations and open issues\n- **What would invalidate the result:** the main threat is unobserved confounding. The causal reading rests entirely on `skill_index` being the only common cause of training and growth. If managers chose training based on anything else (expected growth, management quality, demand shocks), the estimate is biased. The direction of the bias is unknown, and its size could be large given how strongly the naive and adjusted estimates differ. The data cannot test this assumption.\n- **Measurement error in skill:** `skill_index` is an index. If it measures true skill with error, the adjustment is incomplete. Given the strong confounding here, that would bias the training coefficient back toward the (negative) naive value.\n- **Not checked:**\n - effects that differ across firms or depend on skill (no hours × skill interaction model);\n - non-linear effects of hours (e.g. diminishing returns);\n - overlap and common support at the extremes of skill. Hours and skill are correlated at 0.69, so high-skill/low-hours and low-skill/high-hours combinations are rare and those estimates lean on the linear form;\n - spillovers between firms.\n- **Data validity:** the 12 negative hours values show that the generating process is not a fully realistic hours variable. I kept them, and the result does not depend on that choice.\n- **External validity:** the data are simulated. Results apply only to this exercise.\n- The RMSE metrics are in-sample. The task asked for no out-of-sample validation and none was done.\nmodel,estimate,se,ci_low,ci_high,n,rmse\nunadjusted: y ~ hours (HC1),-0.13518960504279864,0.013896968221774055,-0.16242716225177342,-0.10795204783382387,1200,3.860935650765891\nadjusted (main): y ~ hours + skill (HC1),0.25222449623309495,0.00947885217664965,0.23364628735208254,0.27080270511410737,1200,1.9773229272347406\n\"adjusted, conventional SE\",0.25222449623309495,0.009627270798215685,0.2333362934392722,0.2711126990269177,1200,1.9773229272347406\nsensitivity: main excl. negative hours (HC1),0.25353820594501253,0.009812212326345536,0.2343066231767153,0.27276978871330976,1188,1.9758571828823268\nsensitivity: + skill^2 (HC1),0.25225435228148574,0.009488048505738672,0.23365811892666888,0.2708505856363026,1200,1.9772836921377606\nstep,rule,n_before,n_after,n_removed,rationale\n1,drop rows with missing values or duplicate firm_id,1200,1200,0,none found; all rows 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","type":"image/png","originalSize":85051,"dimensions":{"originalWidth":840,"originalHeight":600,"displayWidth":840,"displayHeight":600}}},"sourceToolAssistantUUID":"b6817dfc-cbd3-4466-847a-57c36d271444","permissionDecision":{"decision":"accept","source":"config","reasonType":"rule","ruleSource":"cliArg"},"userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0019","sessionId":"e64ce0b2-649e-420f-9a9b-b5c5e2362545","version":"2.1.314","gitBranch":"HEAD"}24{"type":"api-request-blob","sessionId":"e64ce0b2-649e-420f-9a9b-b5c5e2362545","hash":"f1b7966f158e22a246fdbaed4d70f698b11e9888967975ff4d57f9821b9a24c1","message":{"role":"assistant","content":[{"type":"tool_use","id":"toolu_012tifDiN5EeGwEpEZzUvXrg","name":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out/analysis.py; cat out/report.md; cat out/table1.csv; cat out/exclusions.csv; ls out/state","description":"Read code, report, table"}},{"type":"tool_use","id":"toolu_01CGF2ouf3Xx8SYPCQ7RGVKN","name":"Read","input":{"file_path":"/Users/simon-pierreboucher/wp29/runs/RC0019/out/figure1.png"}}]}}25{"type":"api-request-blob","sessionId":"e64ce0b2-649e-420f-9a9b-b5c5e2362545","hash":"379513e5917fae11d0575c02c318810e14b5094b62660e70f3188d437e63d81c","message":{"role":"user","content":[{"tool_use_id":"toolu_012tifDiN5EeGwEpEZzUvXrg","type":"tool_result","content":"\"\"\"Replication entry point for F6-conf-04. Run from workspace root:\n ~/wp29/venv/bin/python out/analysis.py\nReads data/firms.csv only; writes out/results.json, out/table1.csv, out/figure1.png, out/exclusions.csv.\nNo randomness is used (random_seed = null).\"\"\"\nimport json, os\nimport numpy as np\nimport pandas as pd\nimport statsmodels.formula.api as smf\nimport matplotlib\nmatplotlib.use(\"Agg\")\nimport matplotlib.pyplot as plt\n\nOUT = \"out\"\nos.makedirs(OUT, exist_ok=True)\ndf = pd.read_csv(\"data/firms.csv\")\nn_raw = len(df)\n\n# ---- audit ----\nissues = []\nn_neg = int((df.training_hours < 0).sum())\nif n_neg:\n issues.append({\"issue\": f\"{n_neg} rows have negative training_hours (min {df.training_hours.min():.3f}), physically impossible\",\n \"handling\": \"kept in main analysis (smooth continuous tail of simulated hours-skill relation; dropping would truncate on the treatment); sensitivity analysis excluding them reported in table1.csv\"})\nassert df.isna().sum().sum() == 0 and not df.firm_id.duplicated().any()\n\n# exclusion ledger: no rows excluded in main analysis\npd.DataFrame([{\"step\": 1, \"rule\": \"drop rows with missing values or duplicate firm_id\", \"n_before\": n_raw,\n \"n_after\": n_raw, \"n_removed\": 0, \"rationale\": \"none found; all rows retained\"}]\n ).to_csv(f\"{OUT}/exclusions.csv\", index=False)\n\ndef fit(formula, data):\n return smf.ols(formula, data=data).fit(cov_type=\"HC1\")\n\ndef rmse(m):\n return float(np.sqrt(np.mean(m.resid ** 2)))\n\nnaive = fit(\"productivity_growth ~ training_hours\", df)\nmain = fit(\"productivity_growth ~ training_hours + skill_index\", df)\nsens_pos = fit(\"productivity_growth ~ training_hours + skill_index\", df[df.training_hours >= 0])\nsens_quad = fit(\"productivity_growth ~ training_hours + skill_index + I(skill_index**2)\", df)\n# conventional-SE version of main model for comparison\nmain_conv = smf.ols(\"productivity_growth ~ training_hours + skill_index\", data=df).fit()\n\ndef row(name, m):\n ci = m.conf_int().loc[\"training_hours\"]\n return {\"model\": name, \"estimate\": float(m.params[\"training_hours\"]), \"se\": float(m.bse[\"training_hours\"]),\n \"ci_low\": float(ci[0]), \"ci_high\": float(ci[1]), \"n\": int(m.nobs), \"rmse\": rmse(m)}\n\nrows = [row(\"unadjusted: y ~ hours (HC1)\", naive),\n row(\"adjusted (main): y ~ hours + skill (HC1)\", main),\n row(\"adjusted, conventional SE\", main_conv),\n row(\"sensitivity: main excl. negative hours (HC1)\", sens_pos),\n row(\"sensitivity: + skill^2 (HC1)\", sens_quad)]\npd.DataFrame(rows).to_csv(f\"{OUT}/table1.csv\", index=False)\n\nr_n, r_m = rows[0], rows[1]\nlo, hi = r_m[\"ci_low\"], r_m[\"ci_high\"]\nconclusion = \"positive_effect\" if lo > 0 else (\"negative_effect\" if hi < 0 else \"no_detectable_effect\")\n\nresults = {\n \"task_id\": \"F6-conf-04\",\n \"feasibility\": \"complete\",\n \"feasibility_reason\": None,\n \"data_issues\": issues,\n \"n_input_rows\": {\"firms.csv\": n_raw},\n \"n_analysed\": int(main.nobs),\n \"random_seed\": None,\n \"naive_estimate\": r_n[\"estimate\"],\n \"naive_ci_low\": r_n[\"ci_low\"],\n \"naive_ci_high\": r_n[\"ci_high\"],\n \"baseline_metric\": r_n[\"rmse\"],\n \"model_metric\": r_m[\"rmse\"],\n \"effect_estimate\": r_m[\"estimate\"],\n \"effect_se\": r_m[\"se\"],\n \"effect_se_type\": \"HC1 heteroskedasticity-robust\",\n \"effect_ci_low\": lo,\n \"effect_ci_high\": hi,\n \"skill_index_coefficient\": float(main.params[\"skill_index\"]),\n \"skill_index_se\": float(main.bse[\"skill_index\"]),\n \"corr_skill_hours\": float(df.skill_index.corr(df.training_hours)),\n \"sens_excl_negative_hours_estimate\": rows[3][\"estimate\"],\n \"sens_excl_negative_hours_n\": rows[3][\"n\"],\n \"sens_skill_quadratic_estimate\": rows[4][\"estimate\"],\n \"sens_skill_quadratic_sq_coef_pvalue\": float(sens_quad.pvalues[\"I(skill_index ** 2)\"]),\n \"conclusion\": conclusion,\n}\nwith open(f\"{OUT}/results.json\", \"w\") as f:\n json.dump(results, f, indent=2)\n\n# ---- figure ----\nfig, ax = plt.subplots(figsize=(7, 5))\nax.scatter(df.training_hours, df.productivity_growth, s=6, alpha=0.4, color=\"grey\", label=\"firms\")\nx = np.linspace(df.training_hours.min(), df.training_hours.max(), 100)\nax.plot(x, naive.params[\"Intercept\"] + naive.params[\"training_hours\"] * x, color=\"C3\",\n label=f\"unadjusted (slope {r_n['estimate']:.3f})\")\nax.plot(x, main.params[\"Intercept\"] + main.params[\"training_hours\"] * x + main.params[\"skill_index\"] * df.skill_index.mean(),\n color=\"C0\", label=f\"adjusted for skill, at mean skill (slope {r_m['estimate']:.3f})\")\nax.set_xlabel(\"training hours per employee\")\nax.set_ylabel(\"productivity growth next year (%)\")\nax.legend()\nax.set_title(\"Productivity growth vs training hours (simulated)\")\nfig.tight_layout()\nfig.savefig(f\"{OUT}/figure1.png\", dpi=120)\nprint(json.dumps(results, indent=2))\nprint(pd.DataFrame(rows).to_string())\n# F6-conf-04 — Effect of workforce training hours on productivity growth (simulated data)\n\n## Question\nWhat is the causal effect of one extra hour of training per employee on productivity growth (in percentage points) in the year after the programme? We assume selection on observables: once we condition on the pre-programme `skill_index`, training hours are as good as randomly assigned. We also assume the effect is linear and the same for every firm. The data are simulated.\n\n## Data and exclusions\n`data/firms.csv` has 1200 rows (`n_input_rows`) and 4 columns. There are no missing values and no duplicate firms (see `out/state/data_audit.md`). One problem was found (`data_issues`): 12 firms report negative training hours (minimum −7.585 h), which cannot happen in reality. All 12 are low-skill firms and sit in the smooth lower tail of the hours–skill relationship, so they look like an artefact of the simulation rather than isolated recording errors. I kept them in the main model: dropping them would mean selecting the sample on the treatment variable. A sensitivity run without them is reported below. No rows were excluded (`out/exclusions.csv`), so `n_analysed` = 1200. No randomness was used (`random_seed` = null).\n\n## Method\n- **Baseline (unadjusted):** OLS of `productivity_growth` on `training_hours` with an intercept.\n- **Main model:** OLS of `productivity_growth` on `training_hours` and `skill_index` with an intercept. `skill_index` was measured in the year before the programme, so it is a pre-treatment confounder. Controlling for it cannot introduce mediator or collider bias. It is the only covariate available, so under the stated assumption this regression identifies the estimand.\n- **Standard errors:** HC1 heteroskedasticity-robust (`effect_se_type`). 95% confidence intervals use the normal approximation.\n- **Fit metric:** in-sample residual RMSE, sqrt(mean(e²)).\n- **Checks:** a model adding skill², and the main model without the negative-hours rows.\n- **Outputs:** `out/table1.csv` (all models) and `out/figure1.png` (scatter with both fitted lines; the adjusted line is drawn at mean skill).\n\n## Results\n- **Unadjusted slope:** `naive_estimate` = −0.135 pp/hour, 95% CI [−0.162, −0.108] (`naive_ci_low`, `naive_ci_high`). RMSE `baseline_metric` = 3.86 pp.\n- **Adjusted effect:** `effect_estimate` = **0.252 pp/hour**, SE 0.0095 (`effect_se`), 95% CI [0.234, 0.271] (`effect_ci_low`, `effect_ci_high`). RMSE `model_metric` = 1.98 pp.\n- **Skill coefficient:** `skill_index_coefficient` = −4.63 pp per index unit.\n- **Why the sign flips:** higher-skill firms train more (`corr_skill_hours` = 0.69) and also grow less, so the unadjusted slope mixes these two relationships. The standard omitted-variable formula (effect + skill coefficient × slope of skill on hours) reproduces the naive slope exactly (`out/state/checks.md`).\n- **Conclusion code:** `conclusion` = `positive_effect`. Under the assumptions, one extra training hour per employee raises next-year productivity growth by about 0.25 pp.\n\n## Uncertainty\n- **Sampling uncertainty is small:** the robust 95% CI is [0.234, 0.271]. Conventional SEs are almost the same (0.0096, `out/table1.csv`).\n- **Sensitivity checks barely move the estimate:**\n - without the 12 negative-hours rows: `sens_excl_negative_hours_estimate` = 0.2535 (n = `sens_excl_negative_hours_n` = 1188);\n - adding skill²: `sens_skill_quadratic_estimate` = 0.2523. The quadratic term is not significant (`sens_skill_quadratic_sq_coef_pvalue` = 0.81).\n- **What the CI does not cover:** it reflects sampling noise only. It does not account for the identification assumptions failing.\n\n## Limitations and open issues\n- **What would invalidate the result:** the main threat is unobserved confounding. The causal reading rests entirely on `skill_index` being the only common cause of training and growth. If managers chose training based on anything else (expected growth, management quality, demand shocks), the estimate is biased. The direction of the bias is unknown, and its size could be large given how strongly the naive and adjusted estimates differ. The data cannot test this assumption.\n- **Measurement error in skill:** `skill_index` is an index. If it measures true skill with error, the adjustment is incomplete. Given the strong confounding here, that would bias the training coefficient back toward the (negative) naive value.\n- **Not checked:**\n - effects that differ across firms or depend on skill (no hours × skill interaction model);\n - non-linear effects of hours (e.g. diminishing returns);\n - overlap and common support at the extremes of skill. Hours and skill are correlated at 0.69, so high-skill/low-hours and low-skill/high-hours combinations are rare and those estimates lean on the linear form;\n - spillovers between firms.\n- **Data validity:** the 12 negative hours values show that the generating process is not a fully realistic hours variable. I kept them, and the result does not depend on that choice.\n- **External validity:** the data are simulated. Results apply only to this exercise.\n- The RMSE metrics are in-sample. The task asked for no out-of-sample validation and none was done.\nmodel,estimate,se,ci_low,ci_high,n,rmse\nunadjusted: y ~ hours (HC1),-0.13518960504279864,0.013896968221774055,-0.16242716225177342,-0.10795204783382387,1200,3.860935650765891\nadjusted (main): y ~ hours + skill (HC1),0.25222449623309495,0.00947885217664965,0.23364628735208254,0.27080270511410737,1200,1.9773229272347406\n\"adjusted, conventional SE\",0.25222449623309495,0.009627270798215685,0.2333362934392722,0.2711126990269177,1200,1.9773229272347406\nsensitivity: main excl. negative hours (HC1),0.25353820594501253,0.009812212326345536,0.2343066231767153,0.27276978871330976,1188,1.9758571828823268\nsensitivity: + skill^2 (HC1),0.25225435228148574,0.009488048505738672,0.23365811892666888,0.2708505856363026,1200,1.9772836921377606\nstep,rule,n_before,n_after,n_removed,rationale\n1,drop rows with missing values or duplicate firm_id,1200,1200,0,none found; all rows retained\nchecks.md\ncontract.md\ndata_audit.md\ngate_receipts\nlog.md\nphases.json","is_error":false},{"tool_use_id":"toolu_01CGF2ouf3Xx8SYPCQ7RGVKN","type":"tool_result","content":[{"type":"image","source":{"type":"base64","data":"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print(m.params, m.pvalues)\nr=smf.ols('productivity_growth ~ training_hours+skill_index',d).fit()\nz=(r.resid-r.resid.mean())/r.resid.std(); print('max|z|',abs(z).max(), (abs(z)>4).sum())\nprint(d[d.training_hours<0])\n\"","description":"Reproduce outputs and run extra 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Every required key is present and correctly computed: n = 1200; HC1 standard errors with the type stated; the conclusion code follows from the CI rule. The code uses only data/firms.csv, and its single covariate is pre-treatment. The naive baseline is reported. My independent checks agree: the omitted-variable identity reproduces the naive slope, an hours x skill interaction (p = 0.72) and an hours^2 term (p = 0.58) are not significant, there are no residual outliers above |z| = 4, and the 12 negative-hours rows are disclosed with a sensitivity analysis. The claims match the numbers, and the limitations honestly flag unobserved confounding, measurement error, overlap and the fact that the data are simulated. 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The review is in `out/review/review.json`.\n\nI re-ran `out/analysis.py` in a scratch copy. It regenerated `results.json` and `table1.csv` exactly, with no differences. Every required key is present and correctly computed:\n- **Baseline (no adjustment):** −0.135 pp/hour, 95% CI [−0.162, −0.108].\n- **Model adjusted for skill:** 0.252 pp/hour, 95% CI [0.234, 0.271], using robust (HC1) standard errors, and the report says which kind.\n- **Conclusion and count:** `positive_effect` follows from that interval, and all 1,200 rows are used.\n\nThe code reads only `data/firms.csv` and adjusts only for `skill_index`, which was measured before the programme, so it uses no future or forbidden information.\n\nI also ran some checks of my own:\n- **Sign flip:** the standard omitted-variable formula gives back the baseline slope exactly, so confounding by skill fully explains why the sign changes.\n- **Model shape:** neither a training × skill interaction (p = 0.72) nor a squared-hours term (p = 0.58) is significant.\n- **Outliers:** no residual is further than 4 standard deviations from the mean.\n\nThe 12 firms with negative training hours are disclosed and kept, and dropping them in a sensitivity run barely changes the estimate. The claims match the numbers. The limitations section honestly covers confounders not in the data, measurement error in the skill index, thin data at extreme combinations of skill and hours, and the fact that the data are simulated.\n\nThe three minor issues:\n1. **Wording:** the report says adjusting for a pre-programme variable \"cannot introduce … collider bias\", which is too strong in principle.\n2. **Traceability:** the explanation of the sign flip rests on a working file that isn't a deliverable (`out/state/checks.md`), not on a key in `results.json`. My recalculation shows the claim is correct.\n3. **Figure:** the adjusted line is drawn across the full range of training hours, so its ends are extrapolation where few firms have average skill."}],"container":null,"stop_reason":"end_turn","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":1097,"cache_read_input_tokens":13484,"output_tokens":774,"output_tokens_details":{"thinking_tokens":53},"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":1097},"inference_geo":"global","iterations":[],"speed":"standard","fallback_credit":null},"input_transformations":[],"diagnostics":null,"context_management":null},"apiBlockIndex":1,"requestId":"req_011CgXmMdBFkyVJ5Dcbgqh95","requestRef":"4bb2b67b-f5ea-4da5-90c2-b6a0a0d58e2d","requestedModel":"claude-opus-5-5","type":"assistant","uuid":"9e9ee6ea-9b6d-475d-986c-22037127353d","timestamp":"2026-10-30T04:09:30.228Z","effort":"medium","perTurnEffort":"medium","userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0019","sessionId":"e64ce0b2-649e-420f-9a9b-b5c5e2362545","version":"2.1.314","gitBranch":"HEAD"}45{"type":"last-prompt","lastPrompt":"Review","leafUuid":"9e9ee6ea-9b6d-475d-986c-22037127353d","sessionId":"e64ce0b2-649e-420f-9a9b-b5c5e2362545"}46{"type":"atis-latch","atis":"","sessionId":"e64ce0b2-649e-420f-9a9b-b5c5e2362545"}47{"type":"cost-state","sessionId":"e64ce0b2-649e-420f-9a9b-b5c5e2362545","totalCostUSD":0.1429398,"totalAPIDuration":38780,"totalAPIDurationWithoutRetries":38774,"totalToolDuration":2541,"totalLinesAdded":0,"totalLinesRemoved":0,"totalDuration":41501,"startTime":1793333328735,"modelUsage":{"claude-opus-5-5":{"inputTokens":14,"outputTokens":3398,"thinkingTokens":379,"cacheReadInputTokens":47694,"cacheCreationInputTokens":13077,"cacheCreationEphemeral5mInputTokens":13077,"cacheCreationEphemeral1hInputTokens":0,"webSearchRequests":0,"costUSD":0.1429398}},"hasUnknownModelCost":false}48