{"call_id":"call_2ee529d4","run_id":"20261015T171142Z-suite-causcibench_real-naive-d6494d/causcibench_real.q103/e1a1","step":"analysis","kind":"llm","name":"llm.analyst.scripted","version":"","code_version":"2925aeab2262","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":"2026-10-15T17:11:44.689962Z","duration_ms":0.0,"error":null,"decided_by":"model","rationale":"","cached":false,"informed_by":[],"model":"naive-ols","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\": \"abadie_instrumental_variables\",\n \"rows\": 11204,\n \"description\": \"This dataset was constructed to measure how subsidized Job Training Partnership Act (JTPA) programs affect not just average earnings but different points (quantiles) of the trainee earnings distribution. Data collection process: Data are drawn from the National JTPA Study - a large encouragement design randomized controlled trial under the JTPA. Eligible individuals were randomly assigned to be offered training services (treatment group) or excluded for 18 months (control group), with roughly 60% of those offered actually receiving training. Baseline demographic and economic covariates were collected before randomization, and earnings plus participation data were tracked for 30 months post-assignment via administrative records and follow-up interviews.\\n\\nDataset variables:\\nV1: Unique identifier for individuals\\nincome: Individual's income (continuous, ranges from 0 to ~155,760)\\nJTPA training offer: Whether the person was offered JTPA training (binary: 1 = yes, 0 = no)\\nJTPA training participation: Whether the person actually participated in JTPA training (binary)\\nmale: Gender (1 = male, 0 = female)\\nhsorged: Completed high school or earned a GED (1 = yes, 0 = no)\\nblack: Race indicator (1 = Black, 0 = otherwise)\\nhispanic: Ethnicity indicator (1 = Hispanic, 0 = otherwise)\\nmarried: Marital status (1 = married, 0 = unmarried)\\nwkless13: Employment status: whether worked less than 13 weeks in the year before program entry (1 = yes, 0 = no)\\nafdc: Receipt of Aid to Families with Dependent Children (AFDC welfare program) (1 = yes, 0 = no)\\nage2225: Age group indicator (22-25 years old)\\nage2629: Age group indicator (26-29 years old)\\nage3035: Age group indicator (30-35 years old)\\nage3644: Age group indicator (36-44 years old)\\nage4554: Age group indicator (45-54 years old)\\nclass_tr: Participation in classroom training (1 = yes, 0 = no)\\nojt_jsa: Participation in on-the-job training (OJT) or job search assistance (JSA)\\nf2sms: Indicator for assignment to \\\"\\\"Follow-up survey sample\\\"\\\" or another experimental grouping\",\n \"columns\": {\n  \"Unnamed: 0\": \"int64\",\n  \"V1\": \"float64\",\n  \"income\": \"float64\",\n  \"JTPA training offer\": \"float64\",\n  \"JTPA training participation\": \"float64\",\n  \"male\": \"float64\",\n  \"hsorged\": \"float64\",\n  \"black\": \"float64\",\n  \"hispanic\": \"float64\",\n  \"married\": \"float64\",\n  \"wkless13\": \"float64\",\n  \"afdc\": \"float64\",\n  \"age2225\": \"float64\",\n  \"age2629\": \"float64\",\n  \"age3035\": \"float64\",\n  \"age3644\": \"float64\",\n  \"age4554\": \"float64\",\n  \"class_tr\": \"float64\",\n  \"ojt_jsa\": \"float64\",\n  \"f2sms\": \"float64\"\n }\n}\n\n# Question\nWhat is the effect of receiving subsidized training on 30-month income of men?","response":"Linha de base ingênua: regressão do resultado no tratamento, sem controles.\ninfer__ols({\"dataset\": \"abadie_instrumental_variables\", \"y\": \"income\", \"x\": [\"JTPA training participation\"]})","tokens_in":0,"tokens_out":0,"meta":{"turn_call_id":"call_2ee529d4","text":"Linha de base ingênua: regressão do resultado no tratamento, sem controles.","tool_calls":[{"vendor_id":"script_1","llm_name":"infer__ols","tool_id":"infer.ols","args":{"dataset":"abadie_instrumental_variables","y":"income","x":["JTPA training participation"]}}],"request":{"system_stable_ref":"433a6877aa6a378bbd4b7e2752e484d8cb7499c562d68222be231f62ff2fa420","system_task_ref":"471c73fe08a7f73668e82c9cfe2b7b42a6d768d459753682a28d46dd26c6ffb5","tools_ref":"52ab03d8ec104abb85572294bf8a469d75c5de9e74d6042caabff8017adc572b","contents_refs":[],"messages_refs":["a9e2da0fa0737095d43742af16caa365b55113964f7e01438a28f5d1f76fc0b5"],"config":{}},"attempts":[],"response_id":null,"finish_reason":"tool_use","model_reported":"naive-ols","usage":{"input":0,"output":0,"thoughts":0,"cached":0},"thoughts":null,"vendor":false,"wall_ms":0.855291960760951}}
{"call_id":"call_7d809fdb","run_id":"20261015T171142Z-suite-causcibench_real-naive-d6494d/causcibench_real.q103/e1a1","step":"analysis","kind":"tool","name":"infer.ols","version":"0.1.0","code_version":"2925aeab2262","inputs":{"dataset":"abadie_instrumental_variables","y":"income","x":["JTPA training participation"],"controls":[],"fe":[],"cluster":null,"se":"HC1","weights":null,"subset":null,"intercept":true,"level":0.95},"input_digest":"6cdc61e12c1e75781c64ca63402757ba1a033ddcc34be9a35087892d6267c0ab","dataset_digests":{"abadie_instrumental_variables":"7d1e5077d40b12f4726ea5b1690837de3f5067be68b661660c27c9d21475aa9d"},"result":{"tool":"infer.ols","version":"0.1.0","call_id":"call_7d809fdb","status":"ok","reason":"","figures":[{"key":"coef.JTPA training participation","value":2791.087502531718,"unit":"","label":"Coefficient on JTPA training participation in the regression of income","se":319.8995003824746,"ci_low":2164.0282501199804,"ci_high":3418.146754943456,"ci_level":0.95,"derivation":{"steps":[{"text_pt":"Coeficiente de JTPA training participation na regressão por mínimos quadrados ordinários de income.","formula":"","terms":[{"symbol":"n","value":11204.0,"unit":"observações","note":""},{"symbol":"k","value":2.0,"unit":"parâmetros","note":""}],"result":2791.087502531718,"unit":""},{"text_pt":"Erro-padrão robusto à heterocedasticidade (HC1): a raiz da variância estimada do coeficiente.","formula":"ep = √V_jj","terms":[{"symbol":"V_jj","value":102335.69034495688,"unit":"","note":"diagonal da matriz de covariância"}],"result":319.8995003824746,"unit":""},{"text_pt":"Intervalo de 95% de confiança: a estimativa mais ou menos o valor crítico da distribuição t de Student com 11202 graus de liberdade vezes o erro-padrão.","formula":"IC = β ± c · ep","terms":[{"symbol":"β","value":2791.087502531718,"unit":"","note":""},{"symbol":"c","value":1.9601757791494523,"unit":"","note":"quantil 0.975"},{"symbol":"ep","value":319.8995003824746,"unit":"","note":""}],"result":null,"unit":""}],"note_pt":""}}],"diagnostics":[{"name":"n_obs","value":11204,"flag":false,"note":"observations used"},{"name":"k","value":2,"flag":false,"note":"parameters estimated, fixed-effect dummies included"},{"name":"r2","value":0.006805874461306316,"flag":false,"note":""},{"name":"df_resid","value":11202,"flag":false,"note":""},{"name":"se_type","value":"HC1","flag":false,"note":"heteroskedasticity-robust standard errors (HC1)"},{"name":"n_clusters","value":null,"flag":false,"note":"errors not clustered"}],"tables":{"coefficients":[{"term":"Intercept","coef":14605.085408131106,"se":206.87927768609669,"t":70.59714037812807,"p":0.0,"ci_low":14199.565658802885,"ci_high":15010.605157459328},{"term":"JTPA training participation","coef":2791.087502531718,"se":319.8995003824746,"t":8.724888595307807,"p":3.0407312611019454e-18,"ci_low":2164.0282501199804,"ci_high":3418.146754943456}]},"datasets":{},"warnings":[],"assumptions":["the conditional mean of the outcome is linear in the regressors","errors are independent across observations; their variance may differ"],"notes":""},"outputs":null,"output_digest":"83becf688071a3c680f707d59742f4530afba44bf0f194635e17a6903ad6db76","params_versions":[],"sources":[],"started_at":"2026-10-15T17:11:44.692755Z","duration_ms":4.36033308506012,"error":null,"decided_by":"model","rationale":"Linha de base ingênua: regressão do resultado no tratamento, sem controles.","cached":false,"informed_by":[],"model":null,"prompt":null,"response":null,"tokens_in":null,"tokens_out":null,"meta":{"turn_id":"call_2ee529d4","llm_name":"infer__ols","vendor_id":"script_1","result_for_model_ref":"565ac05a2cbd2a68362bb01a316fb0bee3b5e70687ecab5ac4a888f1d75edef3"}}
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