{"call_id":"call_82c762a8","run_id":"20261015T171142Z-suite-causcibench_real-naive-d6494d/causcibench_real.q011/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":"2026-10-15T17:11:42.951896Z","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\": \"cunningham_the_long_run\",\n \"rows\": 1492,\n \"description\": \"This data examines the effect of abortion legalization on the incidence of gonorrhea among 15-19-year-old females. The core idea is based on a quasi-experiment comparing early repeal states with Roe states. Early repeal states are those that repealed their abortion ban before the Roe v. Wade decision in 1973. Roe states are the states where abortion was legalized after Roe v. Wade.\\n\\nVariable Information:\\n\\nfip: State FIPS code\\nage: Age in years\\nrace: Race (1 = White, 2 = Black)\\nyear: Calendar year\\nt: Year, but counted on a different scale\\nsex: Sex (1 = Male, 2 = Female)\\ntotpop: Total population\\nir: Incarcerated males per 100,000\\ncrack: Crack index\\nalcohol: Alcohol consumption per capita\\nincome: Real income per capita\\nur: State unemployment rate\\npoverty: Poverty rate\\nrepeal: In a state with an early repeal of abortion prohibition\\nacc: AIDS mortality per 100,000 (cumulative in years t, t-1, t-2, t-3)\\nwht: Indicator for White\\nmale: Indicator for Male\\nlnr: Logged gonorrhea cases per 100,000 in 15-19 year olds\\nyounger: Indicator for being in the younger group\\nfa: State-younger interaction\\npi: Parental involvement law in effect\\nbf15: Indicator for Black females in the 15-19 age group\",\n \"columns\": {\n  \"Unnamed: 0\": \"int64\",\n  \"fip\": \"float64\",\n  \"age\": \"float64\",\n  \"race\": \"float64\",\n  \"year\": \"float64\",\n  \"sex\": \"int64\",\n  \"totcase\": \"float64\",\n  \"totpop\": \"int64\",\n  \"rate\": \"float64\",\n  \"totrate\": \"float64\",\n  \"id\": \"float64\",\n  \"ir\": \"float64\",\n  \"crack\": \"float64\",\n  \"alcohol\": \"float64\",\n  \"income\": \"float64\",\n  \"ur\": \"float64\",\n  \"poverty\": \"float64\",\n  \"black\": \"float64\",\n  \"perc1519\": \"float64\",\n  \"perc\": \"float64\",\n  \"repeal\": \"float64\",\n  \"aids\": \"float64\",\n  \"aidscapita\": \"float64\",\n  \"ac\": \"float64\",\n  \"acc\": \"float64\",\n  \"trend\": \"float64\",\n  \"tsq\": \"float64\",\n  \"blk\": \"float64\",\n  \"wht\": \"float64\",\n  \"male\": \"float64\",\n  \"female\": \"float64\",\n  \"lnr\": \"float64\",\n  \"t\": \"float64\",\n  \"younger\": \"float64\",\n  \"fa\": \"float64\",\n  \"pi\": \"float64\",\n  \"wm15\": \"float64\",\n  \"wf15\": \"float64\",\n  \"bm15\": \"float64\",\n  \"bf15\": \"float64\"\n }\n}\n\n# Question\nIs there a difference in gonorrhea rates between early repeal and Roe states among Black females?","response":"Linha de base ingênua: regressão do resultado no tratamento, sem controles.\ninfer__ols({\"dataset\": \"cunningham_the_long_run\", \"y\": \"lnr\", \"x\": [\"repeal\"]})","tokens_in":0,"tokens_out":0,"meta":{"turn_call_id":"call_82c762a8","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":"cunningham_the_long_run","y":"lnr","x":["repeal"]}}],"request":{"system_stable_ref":"433a6877aa6a378bbd4b7e2752e484d8cb7499c562d68222be231f62ff2fa420","system_task_ref":"e74ab188f705f0f21ca4aaa718081061cef77b26f616f8f54cb790cdcbbb8de2","tools_ref":"52ab03d8ec104abb85572294bf8a469d75c5de9e74d6042caabff8017adc572b","contents_refs":[],"messages_refs":["b775f3993fb19cc10c3894c787cf16e7372e7cc98cc1cab612e88905c2d5fd4e"],"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":0.720249954611063}}
{"call_id":"call_c975220a","run_id":"20261015T171142Z-suite-causcibench_real-naive-d6494d/causcibench_real.q011/e1a1","step":"analysis","kind":"tool","name":"infer.ols","version":"0.1.0","code_version":"2925aeab2262","inputs":{"dataset":"cunningham_the_long_run","y":"lnr","x":["repeal"],"controls":[],"fe":[],"cluster":null,"se":"HC1","weights":null,"subset":null,"intercept":true,"level":0.95},"input_digest":"0fcd8e1c8abf350caac568b031cc23cd655e1f71d3051a4ea112136aac2a7411","dataset_digests":{"cunningham_the_long_run":"5d456a9ce8f85c086d28b6a8ca3b08047cbd7e0fd59c3fa3f6cc327fbac96668"},"result":{"tool":"infer.ols","version":"0.1.0","call_id":"call_c975220a","status":"ok","reason":"","figures":[{"key":"coef.repeal","value":-0.774456378260131,"unit":"","label":"Coefficient on repeal in the regression of lnr","se":0.07128521795001623,"ci_low":-0.9142864239708923,"ci_high":-0.6346263325493697,"ci_level":0.95,"derivation":{"steps":[{"text_pt":"Coeficiente de repeal na regressão por mínimos quadrados ordinários de lnr.","formula":"","terms":[{"symbol":"n","value":1492.0,"unit":"observações","note":""},{"symbol":"k","value":2.0,"unit":"parâmetros","note":""}],"result":-0.774456378260131,"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":0.005081582298181316,"unit":"","note":"diagonal da matriz de covariância"}],"result":0.07128521795001623,"unit":""},{"text_pt":"Intervalo de 95% de confiança: a estimativa mais ou menos o valor crítico da distribuição t de Student com 1490 graus de liberdade vezes o erro-padrão.","formula":"IC = β ± c · ep","terms":[{"symbol":"β","value":-0.774456378260131,"unit":"","note":""},{"symbol":"c","value":1.9615573849940025,"unit":"","note":"quantil 0.975"},{"symbol":"ep","value":0.07128521795001623,"unit":"","note":""}],"result":null,"unit":""}],"note_pt":""}}],"diagnostics":[{"name":"n_obs","value":1492,"flag":false,"note":"observations used"},{"name":"k","value":2,"flag":false,"note":"parameters estimated, fixed-effect dummies included"},{"name":"r2","value":0.08372588010557391,"flag":false,"note":""},{"name":"df_resid","value":1490,"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":7.997601963165791,"se":0.021440666852173242,"t":373.01087780089955,"p":0.0,"ci_low":7.9555448647627145,"ci_high":8.039659061568868},{"term":"repeal","coef":-0.774456378260131,"se":0.07128521795001623,"t":-10.864193174006488,"p":1.6342360718723929e-26,"ci_low":-0.9142864239708923,"ci_high":-0.6346263325493697}]},"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":"3cf0f8f7db44f19ab4f2134552f698671cabdf029428be1ff13cb400c8bed27f","params_versions":[],"sources":[],"started_at":"2026-10-15T17:11:42.954764Z","duration_ms":9.77479200810194,"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_82c762a8","llm_name":"infer__ols","vendor_id":"script_1","result_for_model_ref":"a68f052c5dc1908c3851cc43bbecf8c04f9a6168efe9ec80764e918f5dd397e2"}}
{"call_id":"call_b338a638","run_id":"20261015T171142Z-suite-causcibench_real-naive-d6494d/causcibench_real.q011/e1a1","step":"analysis","kind":"llm","name":"llm.analyst.scripted","version":"","code_version":"2925aeab2262","inputs":{},"input_digest":"","dataset_digests":{},"result":null,"outputs":null,"output_digest":"","params_versions":[],"sources":[],"started_at":"2026-10-15T17:11:42.964693Z","duration_ms":0.0,"error":null,"decided_by":"model","rationale":"","cached":false,"informed_by":[],"model":"naive-ols","prompt":null,"response":"submit_answer({\"answer\": \"Estimativa ingênua (infer.ols): coef.repeal = -0,7745.\", \"figure_refs\": [\"call_c975220a:coef.repeal\"], \"caveats\": []})","tokens_in":0,"tokens_out":0,"meta":{"turn_call_id":"call_b338a638","text":"","tool_calls":[{"vendor_id":"script_2","llm_name":"submit_answer","tool_id":null,"args":{"answer":"Estimativa ingênua (infer.ols): coef.repeal = -0,7745.","figure_refs":["call_c975220a:coef.repeal"],"caveats":[]}}],"request":{"system_stable_ref":"433a6877aa6a378bbd4b7e2752e484d8cb7499c562d68222be231f62ff2fa420","system_task_ref":"e74ab188f705f0f21ca4aaa718081061cef77b26f616f8f54cb790cdcbbb8de2","tools_ref":"52ab03d8ec104abb85572294bf8a469d75c5de9e74d6042caabff8017adc572b","contents_refs":[],"messages_refs":["b775f3993fb19cc10c3894c787cf16e7372e7cc98cc1cab612e88905c2d5fd4e","95dc80eb834568969be8883ce7e7bcf96c145584e98851b19235d2fa38802119","32372447872ea592656b3844fdd7dc7752122ec9b8cdfe6fec93a1c139a31f52"],"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":0.20579202100634575}}
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