{"call_id":"call_7e1d5f8c","run_id":"20261015T165744Z-suite-paper_repro-scripted-def9df/paper_repro.dehejia_wahba_1999.tab2_pb_c1.design/e1a1","step":"analysis","kind":"llm","name":"llm.analyst.scripted","version":"","code_version":"62cdbe0860ad","inputs":{"tools":["calc__annuity","calc__breakeven","calc__combine","calc__discount","calc__fiscal_impact","core__atkinson","core__equivalent_variation","core__fgt","core__gini","core__indirect_incidence","core__kakwani","core__mvpf","core__palma","core__quantile_shares","core__reynolds_smolensky","core__scale_effect","core__theil","core__welfare_change","data__cite","data__describe","data__siconfi","data__sidra","evidence__pool_dl","infer__ate","infer__density_test","infer__did_2x2","infer__did_staggered","infer__event_study","infer__fe","infer__fisher_exact","infer__iv_2sls","infer__linear_hypothesis","infer__mde","infer__neyman","infer__ols","infer__power","infer__randomization_inference","infer__rdd","infer__sample_size","infer__twfe","infer__wald_late","submit_answer"],"exemplars":[]},"input_digest":"","dataset_digests":{},"result":null,"outputs":null,"output_digest":"","params_versions":[],"sources":[],"started_at":"2026-10-15T16:57:55.818703Z","duration_ms":0.0,"error":null,"decided_by":"model","rationale":"","cached":false,"informed_by":[],"model":"reference","prompt":"# Role\nYou are MAGI's economist. A municipal analyst in Brazil asks a question; you answer it by choosing registered tools and their arguments. The tools compute every number. Your job is to recognise how the data were generated, pick the tool whose design matches, read what it reports, and say what the result means for the analyst.\n\n# Rules\n1. You never compute a number. Every number in your answer must have been stated by a tool call in this run (a figure, its standard error or interval, a diagnostic, a table cell) or written in the question. No mental arithmetic: for a ratio, difference, sum, product, percent change or share of figures already computed, call calc.combine; to restate a log-point coefficient as a percent or an effect in standard deviations, call core.scale_effect.\n2. Before estimating, work out the design: how units came to be treated (a lottery, an offer with partial take-up, a threshold of a running variable, a policy that started on one date or on different dates, self-selection), what a row is, and which columns hold the outcome, treatment, unit, time, running variable, instrument, cluster and survey weight. Call data.describe when the columns are unclear. Then choose the tool whose \"Use when\" matches and heed its \"Avoid when\".\n3. Read the diagnostics, warnings and \"Check after\" items a result returns before you trust a figure. If a diagnostic says the design fails — a weak instrument, sorting at the cutoff, few clusters, non-parallel pre-trends — say so plainly and do not present the estimate as causal.\n4. If no tool fits the design, or the data cannot identify what is asked, decline and say what is missing. Never invent a design, a column or a number.\n5. A tool that errors or declines returns its reason; fix the arguments and call again, or choose another tool.\n6. Before each tool call, write one sentence on why this tool and these arguments fit the design. It is recorded as your rationale.\n7. Finish with submit_answer. Write the answer in the language of the question (Portuguese: decimal comma), short and direct: the estimate with its uncertainty, the design that identifies it, and what could undermine it. List in figure_refs every figure you quote, as '<call_id>:<key>' (each tool result carries its call_id), and put diagnostics that limit the conclusion in caveats. Write numbers as the tools stated them; do not quote rule-of-thumb thresholds as numbers.\n\n# Datasets\nPass a dataset's id as the `dataset` argument.\n{\n \"id\": \"nsw_dw\",\n \"rows\": 445,\n \"description\": \"Dehejia and Wahba's RE74 subsample of LaLonde's National Supported Work experimental sample: the men for whom 1974 earnings are available, as posted by Rajeev Dehejia (nsw_dw.dta): one row per man (445 rows: the treated and the experimental controls). Columns: data_id, the sample label; treat, 1 for men randomly assigned to the NSW job training program and 0 for the experimental controls; age, in years; education, years of schooling; black, 1 if black; hispanic, 1 if Hispanic; married, 1 if married; nodegree, 1 for no high school degree; re74, re75 and re78, real earnings in 1974, 1975 and 1978 (1982 dollars).\",\n \"columns\": {\n  \"data_id\": \"str\",\n  \"treat\": \"float32\",\n  \"age\": \"float32\",\n  \"education\": \"float32\",\n  \"black\": \"float32\",\n  \"hispanic\": \"float32\",\n  \"married\": \"float32\",\n  \"nodegree\": \"float32\",\n  \"re74\": \"float32\",\n  \"re75\": \"float32\",\n  \"re78\": \"float32\"\n }\n}\n\n# Question\nDid the National Supported Work job-training program raise the 1978 earnings of the men randomly assigned to it? Use Dehejia and Wahba's experimental subsample in the file (the men with 1974 earnings available) and report the effect in 1982 dollars.","response":"Table 2, Panel B, column (1), NSW row: the experimental difference in 1978 earnings in the RE74 subsample, as the OLS coefficient on the treatment indicator, classical errors.\ninfer__ols({\"dataset\": \"nsw_dw\", \"y\": \"re78\", \"x\": [\"treat\"], \"controls\": [], \"fe\": [], \"se\": \"classical\"})","tokens_in":0,"tokens_out":0,"meta":{"turn_call_id":"call_7e1d5f8c","text":"Table 2, Panel B, column (1), NSW row: the experimental difference in 1978 earnings in the RE74 subsample, as the OLS coefficient on the treatment indicator, classical errors.","tool_calls":[{"vendor_id":"script_1","llm_name":"infer__ols","tool_id":"infer.ols","args":{"dataset":"nsw_dw","y":"re78","x":["treat"],"controls":[],"fe":[],"se":"classical"}}],"request":{"system_stable_ref":"433a6877aa6a378bbd4b7e2752e484d8cb7499c562d68222be231f62ff2fa420","system_task_ref":"a6f010621d9aaa1b1a743a36ea2b326967b2fb4e606a950a4db45568b2bf84eb","tools_ref":"52ab03d8ec104abb85572294bf8a469d75c5de9e74d6042caabff8017adc572b","contents_refs":[],"messages_refs":["c12cf2090f39115b9fc30eb0bf711cfd5e83e86edf7bc68a274e55f1f39f9e28"],"config":{}},"attempts":[],"response_id":null,"finish_reason":"tool_use","model_reported":"reference","usage":{"input":0,"output":0,"thoughts":0,"cached":0},"thoughts":null,"vendor":false,"wall_ms":0.7637080270797014}}
{"call_id":"call_b4c2c3cd","run_id":"20261015T165744Z-suite-paper_repro-scripted-def9df/paper_repro.dehejia_wahba_1999.tab2_pb_c1.design/e1a1","step":"analysis","kind":"tool","name":"infer.ols","version":"0.1.0","code_version":"62cdbe0860ad","inputs":{"dataset":"nsw_dw","y":"re78","x":["treat"],"controls":[],"fe":[],"cluster":null,"se":"classical","weights":null,"subset":null,"intercept":true,"level":0.95},"input_digest":"33d3d529c6817902e684907269aff720b0c8baee1c3d3a9d32e3c84da0b65f84","dataset_digests":{"nsw_dw":"9d87f83bd01c3b170c9640ba069449cffe5b94b9ef4bd0a4d8ad71f75b163728"},"result":{"tool":"infer.ols","version":"0.1.0","call_id":"call_b4c2c3cd","status":"ok","reason":"","figures":[{"key":"coef.treat","value":1794.342381850102,"unit":"","label":"Coefficient on treat in the regression of re78","se":632.8533924143646,"ci_low":550.5744659319503,"ci_high":3038.1102977682535,"ci_level":0.95,"derivation":{"steps":[{"text_pt":"Coeficiente de treat na regressão por mínimos quadrados ordinários de re78.","formula":"","terms":[{"symbol":"n","value":445.0,"unit":"observações","note":""},{"symbol":"k","value":2.0,"unit":"parâmetros","note":""}],"result":1794.342381850102,"unit":""},{"text_pt":"Erro-padrão clássico (homocedástico): a raiz da variância estimada do coeficiente.","formula":"ep = √V_jj","terms":[{"symbol":"V_jj","value":400503.4162903698,"unit":"","note":"diagonal da matriz de covariância"}],"result":632.8533924143646,"unit":""},{"text_pt":"Intervalo de 95% de confiança: a estimativa mais ou menos o valor crítico da distribuição t de Student com 443 graus de liberdade vezes o erro-padrão.","formula":"IC = β ± c · ep","terms":[{"symbol":"β","value":1794.342381850102,"unit":"","note":""},{"symbol":"c","value":1.9653334102755144,"unit":"","note":"quantil 0.975"},{"symbol":"ep","value":632.8533924143646,"unit":"","note":""}],"result":null,"unit":""}],"note_pt":""}}],"diagnostics":[{"name":"n_obs","value":445,"flag":false,"note":"observations used"},{"name":"k","value":2,"flag":false,"note":"parameters estimated, fixed-effect dummies included"},{"name":"r2","value":0.017823388243306315,"flag":false,"note":""},{"name":"df_resid","value":443,"flag":false,"note":""},{"name":"se_type","value":"classical","flag":false,"note":"classical (homoskedastic) standard errors"},{"name":"n_clusters","value":null,"flag":false,"note":"errors not clustered"}],"tables":{"coefficients":[{"term":"Intercept","coef":4554.801120215196,"se":408.0458555794276,"t":11.162473672835986,"p":1.1541139790232615e-25,"ci_low":3752.854967320489,"ci_high":5356.747273109902},{"term":"treat","coef":1794.342381850102,"se":632.8533924143646,"t":2.8353207920788788,"p":0.004787530021030672,"ci_low":550.5744659319503,"ci_high":3038.1102977682535}]},"datasets":{},"warnings":[],"assumptions":["the conditional mean of the outcome is linear in the regressors","errors are independent and have the same variance for every observation"],"notes":""},"outputs":null,"output_digest":"a754bf4cb541e3e0c191b8f769f49f1a75204fec432bbd72577acc02fe802f3b","params_versions":[],"sources":[],"started_at":"2026-10-15T16:57:55.821220Z","duration_ms":2.253458136692643,"error":null,"decided_by":"model","rationale":"Table 2, Panel B, column (1), NSW row: the experimental difference in 1978 earnings in the RE74 subsample, as the OLS coefficient on the treatment indicator, classical errors.","cached":false,"informed_by":[],"model":null,"prompt":null,"response":null,"tokens_in":null,"tokens_out":null,"meta":{"turn_id":"call_7e1d5f8c","llm_name":"infer__ols","vendor_id":"script_1","result_for_model_ref":"7db00156a176ed1656286af8e3f004cffd2bcb6f33cc9dabadfc0740732f199b"}}
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