{"call_id":"call_47b6ee2e","run_id":"20261015T171142Z-suite-causcibench_real-naive-d6494d/causcibench_real.q130/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":"2026-10-15T17:11:45.148096Z","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\": \"deuchert_direct\",\n \"rows\": 4968,\n \"description\": \"The study analyzes the impact of the Vietnam draft lottery on political preferences, recognizing that lottery assignment may have influenced attitudes through multiple channels beyond military service alone. Not everyone chosen by the lottery actually served, creating variation in both draft exposure and service status. \\nInterviews were conducted in wave 4 (prior to July 1, 1970 lottery) and wave 5 (spring 1974), allowing examination of attitude changes over time. Each row contains an individual's political preferences in a given wave. Focus on waves 4 and 5. \\nThe direct effect captures how lottery assignment affected political preferences independent of military service—through heightened political awareness, life planning changes, or social identity shifts. The indirect effect operates through actual military service experience.\\n\\nVariables in the dataset:\\n\\nwave: phase when the political preferences were measured\\ntreatment1: whether the individual actually served in military\\nceiling: 1 if the individual was chosen by the draft lottery, 0 otherwise\\nid: person identifier (used for the estimating standard errors via bootstrap)\\nrepu: whether the individual describes themselves as a mild republican\\nsrepu: whether the individual describes themselves as a strong republican\\ndemo: whether the individual describes themselves as a democrat\\nsdemo: whether the individual describes themselves as a strong democrat\\npolalient: political alienation, how skeptical the individual is about the government\\nVietnam: how skeptical the individual is about the Vietnam war\\nERindex: preferences for civil rights interventions\",\n \"columns\": {\n  \"id\": \"float64\",\n  \"treatment1\": \"float64\",\n  \"V3\": \"float64\",\n  \"V4\": \"float64\",\n  \"yob\": \"float64\",\n  \"draftnr\": \"int64\",\n  \"ceiling\": \"float64\",\n  \"srepu\": \"float64\",\n  \"sdemo\": \"float64\",\n  \"repu\": \"float64\",\n  \"demo\": \"float64\",\n  \"polalient\": \"float64\",\n  \"Vietnam\": \"float64\",\n  \"ERindex\": \"float64\",\n  \"wave\": \"float64\",\n  \"mc_student\": \"float64\",\n  \"mc_avail\": \"float64\",\n  \"mc_notcl\": \"float64\",\n  \"mc_other\": \"float64\",\n  \"milknow\": \"float64\",\n  \"iqtest\": \"float64\",\n  \"spiq\": \"float64\",\n  \"collplan\": \"float64\"\n }\n}\n\n# Question\nWhat is the effect of being drafted on the compliers among those who comply with the draft lottery, explained by serving in the military, with respect to holding mild democrat views?","response":"Linha de base ingênua: regressão do resultado no tratamento, sem controles.\ninfer__ols({\"dataset\": \"deuchert_direct\", \"y\": \"demo\", \"x\": [\"ceiling\"]})","tokens_in":0,"tokens_out":0,"meta":{"turn_call_id":"call_47b6ee2e","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":"deuchert_direct","y":"demo","x":["ceiling"]}}],"request":{"system_stable_ref":"433a6877aa6a378bbd4b7e2752e484d8cb7499c562d68222be231f62ff2fa420","system_task_ref":"db554270823b00fbc8b61b4ff3cd1e79792050a4794e9d3b660cb977892abe1a","tools_ref":"52ab03d8ec104abb85572294bf8a469d75c5de9e74d6042caabff8017adc572b","contents_refs":[],"messages_refs":["7f333ee038dcd0fc85be6c593c1a8733f3211cb19a3b4ca6ff55de5977c3bf35"],"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":0.7175419013947248}}
{"call_id":"call_63cd85e8","run_id":"20261015T171142Z-suite-causcibench_real-naive-d6494d/causcibench_real.q130/e1a1","step":"analysis","kind":"tool","name":"infer.ols","version":"0.1.0","code_version":"2925aeab2262","inputs":{"dataset":"deuchert_direct","y":"demo","x":["ceiling"],"controls":[],"fe":[],"cluster":null,"se":"HC1","weights":null,"subset":null,"intercept":true,"level":0.95},"input_digest":"18aaea3a054c7a8470c3151b29f1794bc63d81ef9234d91ad02f548b0797f2d1","dataset_digests":{"deuchert_direct":"39244b0e48fedebdf1241f7685b70a8ee1eacc73a96e66a6e07a3a8a4cfe65a7"},"result":{"tool":"infer.ols","version":"0.1.0","call_id":"call_63cd85e8","status":"ok","reason":"","figures":[{"key":"coef.ceiling","value":-0.02044589884800596,"unit":"","label":"Coefficient on ceiling in the regression of demo","se":0.015249800392789377,"ci_low":-0.05034303387919901,"ci_high":0.009451236183187085,"ci_level":0.95,"derivation":{"steps":[{"text_pt":"Coeficiente de ceiling na regressão por mínimos quadrados ordinários de demo.","formula":"","terms":[{"symbol":"n","value":4483.0,"unit":"observações","note":""},{"symbol":"k","value":2.0,"unit":"parâmetros","note":""}],"result":-0.02044589884800596,"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":0.00023255641201991902,"unit":"","note":"diagonal da matriz de covariância"}],"result":0.015249800392789377,"unit":""},{"text_pt":"Intervalo de 95% de confiança: a estimativa mais ou menos o valor crítico da distribuição t de Student com 4481 graus de liberdade vezes o erro-padrão.","formula":"IC = β ± c · ep","terms":[{"symbol":"β","value":-0.02044589884800596,"unit":"","note":""},{"symbol":"c","value":1.9604935317926802,"unit":"","note":"quantil 0.975"},{"symbol":"ep","value":0.015249800392789377,"unit":"","note":""}],"result":null,"unit":""}],"note_pt":""}}],"diagnostics":[{"name":"n_obs","value":4483,"flag":false,"note":"observations used"},{"name":"k","value":2,"flag":false,"note":"parameters estimated, fixed-effect dummies included"},{"name":"r2","value":0.00039765951571968383,"flag":false,"note":""},{"name":"df_resid","value":4481,"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":0.37254901960784315,"se":0.008815915465543988,"t":42.258687831559754,"p":0.0,"ci_low":0.3552654743608131,"ci_high":0.38983256485487316},{"term":"ceiling","coef":-0.02044589884800596,"se":0.015249800392789377,"t":-1.3407322273984306,"p":0.18007536920939565,"ci_low":-0.05034303387919901,"ci_high":0.009451236183187085}]},"datasets":{},"warnings":["485 rows were dropped because one of 'demo', 'ceiling' was missing"],"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":"3de36ebfcfcfe5e8b230f3926662f615ab19c3da45ff0a221ca474274172865d","params_versions":[],"sources":[],"started_at":"2026-10-15T17:11:45.151110Z","duration_ms":24.97012517414987,"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_47b6ee2e","llm_name":"infer__ols","vendor_id":"script_1","result_for_model_ref":"5bb7da508ffb928ae185cb49e693ea06ff5248669767d0f2cd2101aec8b7984a"}}
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