{"call_id":"call_d8f9b2ec","run_id":"20261015T171142Z-suite-causcibench_real-naive-d6494d/causcibench_real.q173/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":"2026-10-15T17:11:47.485954Z","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\": \"carpenter_the_minimum_legal_drinking\",\n \"rows\": 2922,\n \"description\": \"This dataset contains comprehensive arrest and criminal justice records from California covering individuals around their 21st birthday. The data captures detailed information on various types of criminal offenses, demographic characteristics, and temporal variables that enable analysis of crime patterns across different age groups. The dataset includes both raw count data and population-adjusted rates for multiple categories of criminal activity, spanning from serious felonies to minor infractions and regulatory violations.\\n\\nVariable Descriptions:\\ndays_to_21: Number of days until or since an individual's 21st birthday\\nyears_to_21: Years until or since turning 21\\nall: Total number of arrests across all offense categories\\nfelony: Count of felony-level arrests\\nmisdemeanor: Count of misdemeanor-level arrests\\nfbi_offense_miss: FBI-classified misdemeanor offenses\\nviolent: Total violent crime arrests\\nmurder: Homicide arrests\\nmanslaughter: Manslaughter arrests\\nrape: Sexual assault arrests\\nrobbery: Robbery arrests\\nassault: Total assault arrests\\naggravated_assault: Aggravated assault arrests\\not_assault: Other types of assault\\nproperty: Total property crime arrests\\nburglary: Burglary arrests\\nlarceny: Theft arrests\\nmv_theft: Motor vehicle theft arrests\\nstolen_prop_buy_rec_poss: Receiving stolen property arrests\\nvandalism: Property damage arrests\\nill_drugs: Total drug-related arrests\\ncocaine_opio_sale_manuf: Cocaine/opiate sales and manufacturing arrests\\nmj_sale_manuf: Marijuana sales and manufacturing arrests\\ndang_non_narc_sale_manuf: Other dangerous drug sales/manufacturing arrests\\ncocaine_opio_posses: Cocaine/opiate possession arrests\\nmj_posses: Marijuana possession arrests\\ndang_non_narc_posses: Other dangerous drug possession arrests\\nalcohol: Total alcohol-related arrests\\ndrunk_at_risk: Public intoxication arrests\\ndui: Driving under the influence arrests\\nliquor_laws: Liquor law violation arrests\\ndrunkeness_pc: Public drunkenness arrests\\ndisorderly_cond: Disorderly conduct arrests\\nvagrancy: Vagrancy arrests\\narson: Arson arrests\\nforgery_counterfit: Forgery and counterfeiting arrests\\nfraud: Fraud arrests\\nembezelment: Embezzlement arrests\\nweapons: Weapons violations\\nprostitution: Prostitution arrests\\nsex_offenses: Sexual offense arrests\\nbookmaking: Illegal gambling arrests\\nall_other_gambling: Other gambling violations\\naga_family_child: Offenses against family and children\\ncurfew_loitering: Curfew and loitering violations\\nrunaways: Runaway arrests\\ncoded_on_offense: Arrests coded by primary offense\\ncounty_ordinance: County ordinance violations\\noutside_warrent: Out-of-jurisdiction warrant arrests\\ntraffic_violations: Traffic violation arrests\\nhit_run_reckl_driv: Hit and run/reckless driving arrests\\nparole_violation: Parole violation arrests\\ndump_or_litter: Illegal dumping/littering violations\\ntrespass: Trespassing arrests\\nviolate_court_order: Court order violations\\nfailure_to_appear: Failure to appear in court\\ncont_delinq_minor: Contributing to the delinquency of a minor\\nnot_classified: Unclassified arrests\\nother_reported: Other reported offenses\\npop1, pop2, pop3: Population counts for different demographic groups\\npop_day1, pop_day2, pop_day3: Daily population estimates\\npost: Binary indicator variable that is 1 if age >= 21, i.e., the individual is 21\\nlinear, square, cubic: years_to_21 raised to the power of 1, 2, and 3 respectively\\nlinear_post, square_post, cubic_post: Interaction of linear, square, cubic with post\\nbirthday_19, birthday_20, birthday_21, birthday_22, birthday_23: Dummy variables for 19th, 20th, 21st, 22nd, and 23rd birthdays\\nbirthday_19_1, birthday_20_1, birthday_21_1, birthday_22_1, birthday_23_1: Dummy variables for a day after the 19th, 20th, 21st, 22nd, and 23rd birthdays\\nAll variables with _r suffix represent population-adjusted rates corresponding to their count variable counterparts. These include crime-specific rates such as violent_r, property_r, alcohol_r, dui_r, and all_r, among others. The rate variables normalize the raw counts by relevant population denominators.\",\n \"columns\": {\n  \"days_to_21\": \"float64\",\n  \"all\": \"float64\",\n  \"felony\": \"float64\",\n  \"misdemeanor\": \"float64\",\n  \"fbi_offense_miss\": \"float64\",\n  \"violent\": \"float64\",\n  \"murder\": \"float64\",\n  \"manslaughter\": \"float64\",\n  \"rape\": \"float64\",\n  \"robbery\": \"float64\",\n  \"assault\": \"float64\",\n  \"aggravated_assault\": \"float64\",\n  \"ot_assault\": \"float64\",\n  \"property\": \"float64\",\n  \"burglary\": \"float64\",\n  \"larceny\": \"float64\",\n  \"mv_theft\": \"float64\",\n  \"stolen_prop_buy_rec_poss\": \"float64\",\n  \"vandalism\": \"float64\",\n  \"ill_drugs\": \"float64\",\n  \"cocaine_opio_sale_manuf\": \"float64\",\n  \"mj_sale_manuf\": \"float64\",\n  \"dang_non_narc_sale_manuf\": \"float64\",\n  \"cocaine_opio_posses\": \"float64\",\n  \"mj_posses\": \"float64\",\n  \"dang_non_narc_posses\": \"float64\",\n  \"alcohol\": \"float64\",\n  \"drunk_at_risk\": \"float64\",\n  \"dui\": \"float64\",\n  \"liquor_laws\": \"float64\",\n  \"drunkeness_pc\": \"float64\",\n  \"disorderly_cond\": \"float64\",\n  \"vagrancy\": \"float64\",\n  \"fbi_other\": \"float64\",\n  \"arson\": \"float64\",\n  \"forgery_counterfit\": \"float64\",\n  \"fraud\": \"float64\",\n  \"embezelment\": \"float64\",\n  \"weapons\": \"float64\",\n  \"prostitution\": \"float64\",\n  \"sex_offenses\": \"float64\",\n  \"bookmaking\": \"float64\",\n  \"all_other_gambling\": \"float64\",\n  \"aga_family_child\": \"float64\",\n  \"curfew_loitering\": \"float64\",\n  \"runaways\": \"float64\",\n  \"coded_on_offense\": \"float64\",\n  \"county_ordinance\": \"float64\",\n  \"outside_warrent\": \"float64\",\n  \"traffic_violations\": \"float64\",\n  \"hit_run_reckl_driv\": \"float64\",\n  \"parole_violation\": \"float64\",\n  \"dump_or_litter\": \"float64\",\n  \"trespass\": \"float64\",\n  \"violate_court_order\": \"float64\",\n  \"failure_to_appear\": \"float64\",\n  \"cont_delinq_minor\": \"float64\",\n  \"not_classified\": \"float64\",\n  \"other_reported\": \"float64\",\n  \"pop1\": \"float64\",\n  \"pop2\": \"float64\",\n  \"pop3\": \"float64\",\n  \"pop_day1\": \"float64\",\n  \"pop_day2\": \"float64\",\n  \"pop_day3\": \"float64\",\n  \"years_to_21\": \"float64\",\n  \"yrs_to_21_month\": \"float64\",\n  \"yrs_to_21_fortnight\": \"float64\",\n  \"post\": \"float64\",\n  \"linear\": \"float64\",\n  \"square\": \"float64\",\n  \"cubic\": \"float64\",\n  \"linear_post\": \"float64\",\n  \"square_post\": \"float64\",\n  \"cubic_post\": \"float64\",\n  \"birthday_19\": \"float64\",\n  \"birthday_19_1\": \"float64\",\n  \"birthday_20\": \"float64\",\n  \"birthday_20_1\": \"float64\",\n  \"birthday_21\": \"float64\",\n  \"birthday_21_1\": \"float64\",\n  \"birthday_22\": \"float64\",\n  \"birthday_22_1\": \"float64\",\n  \"birthday_23\": \"float64\",\n  \"birthday_23_1\": \"float64\",\n  \"ill_drugs_r\": \"float64\",\n  \"cocaine_opio_sale_manuf_r\": \"float64\",\n  \"mj_sale_manuf_r\": \"float64\",\n  \"dang_non_narc_sale_manuf_r\": \"float64\",\n  \"cocaine_opio_posses_r\": \"float64\",\n  \"mj_posses_r\": \"float64\",\n  \"dang_non_narc_posses_r\": \"float64\",\n  \"dui_r\": \"float64\",\n  \"liquor_laws_r\": \"float64\",\n  \"drunkeness_pc_r\": \"float64\",\n  \"felony_r\": \"float64\",\n  \"misdemeanor_r\": \"float64\",\n  \"violent_r\": \"float64\",\n  \"murder_r\": \"float64\",\n  \"manslaughter_r\": \"float64\",\n  \"rape_r\": \"float64\",\n  \"robbery_r\": \"float64\",\n  \"assault_r\": \"float64\",\n  \"aggravated_assault_r\": \"float64\",\n  \"ot_assault_r\": \"float64\",\n  \"property_r\": \"float64\",\n  \"burglary_r\": \"float64\",\n  \"larceny_r\": \"float64\",\n  \"mv_theft_r\": \"float64\",\n  \"stolen_prop_buy_rec_poss_r\": \"float64\",\n  \"vandalism_r\": \"float64\",\n  \"disorderly_cond_r\": \"float64\",\n  \"vagrancy_r\": \"float64\",\n  \"fbi_other_r\": \"float64\",\n  \"arson_r\": \"float64\",\n  \"forgery_counterfit_r\": \"float64\",\n  \"fraud_r\": \"float64\",\n  \"embezelment_r\": \"float64\",\n  \"weapons_r\": \"float64\",\n  \"prostitution_r\": \"float64\",\n  \"sex_offenses_r\": \"float64\",\n  \"bookmaking_r\": \"float64\",\n  \"all_other_gambling_r\": \"float64\",\n  \"aga_family_child_r\": \"float64\",\n  \"curfew_loitering_r\": \"float64\",\n  \"runaways_r\": \"float64\",\n  \"county_ordinance_r\": \"float64\",\n  \"traffic_violations_r\": \"float64\",\n  \"hit_run_reckl_driv_r\": \"float64\",\n  \"dump_or_litter_r\": \"float64\",\n  \"trespass_r\": \"float64\",\n  \"violate_court_order_r\": \"float64\",\n  \"failure_to_appear_r\": \"float64\",\n  \"cont_delinq_minor_r\": \"float64\",\n  \"not_classified_r\": \"float64\",\n  \"drunk_at_risk_r\": \"float64\",\n  \"outside_warrent_r\": \"float64\",\n  \"parole_violation_r\": \"float64\",\n  \"other_reported_r\": \"float64\",\n  \"coded_on_offense_r\": \"float64\",\n  \"alcohol_r\": \"float64\",\n  \"all_r\": \"float64\",\n  \"other_r\": \"float64\"\n }\n}\n\n# Question\nHow does gaining legal access to alcohol at age 21 affect alcohol related crime arrest rates?","response":"Linha de base ingênua: regressão do resultado no tratamento, sem controles.\ninfer__ols({\"dataset\": \"carpenter_the_minimum_legal_drinking\", \"y\": \"alcohol_r\", \"x\": [\"post\"]})","tokens_in":0,"tokens_out":0,"meta":{"turn_call_id":"call_d8f9b2ec","text":"Linha de base ingênua: regressão do resultado no tratamento, sem 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{"call_id":"call_1eb176a1","run_id":"20261015T171142Z-suite-causcibench_real-naive-d6494d/causcibench_real.q173/e1a1","step":"analysis","kind":"tool","name":"infer.ols","version":"0.1.0","code_version":"2925aeab2262","inputs":{"dataset":"carpenter_the_minimum_legal_drinking","y":"alcohol_r","x":["post"],"controls":[],"fe":[],"cluster":null,"se":"HC1","weights":null,"subset":null,"intercept":true,"level":0.95},"input_digest":"ff880b043d6dff50de9b87432e28a06248241b0b2784a9c7dee9aa9b77f4ff0c","dataset_digests":{"carpenter_the_minimum_legal_drinking":"c3e530c6e7505a6b45c809a4b7110c6ff1bcf4917d532cba2bd1fc079433bde4"},"result":{"tool":"infer.ols","version":"0.1.0","call_id":"call_1eb176a1","status":"ok","reason":"","figures":[{"key":"coef.post","value":70.22866273024319,"unit":"","label":"Coefficient on post in the regression of alcohol_r","se":2.233681964388368,"ci_low":65.84891109588257,"ci_high":74.6084143646038,"ci_level":0.95,"derivation":{"steps":[{"text_pt":"Coeficiente de post na regressão por mínimos quadrados ordinários de alcohol_r.","formula":"","terms":[{"symbol":"n","value":2922.0,"unit":"observações","note":""},{"symbol":"k","value":2.0,"unit":"parâmetros","note":""}],"result":70.22866273024319,"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":4.989335118033879,"unit":"","note":"diagonal da matriz de covariância"}],"result":2.233681964388368,"unit":""},{"text_pt":"Intervalo de 95% de confiança: a estimativa mais ou menos o valor crítico da distribuição t de Student com 2920 graus de liberdade vezes o erro-padrão.","formula":"IC = β ± c · ep","terms":[{"symbol":"β","value":70.22866273024319,"unit":"","note":""},{"symbol":"c","value":1.960776737327463,"unit":"","note":"quantil 0.975"},{"symbol":"ep","value":2.233681964388368,"unit":"","note":""}],"result":null,"unit":""}],"note_pt":""}}],"diagnostics":[{"name":"n_obs","value":2922,"flag":false,"note":"observations used"},{"name":"k","value":2,"flag":false,"note":"parameters estimated, fixed-effect dummies included"},{"name":"r2","value":0.2529144345478368,"flag":false,"note":""},{"name":"df_resid","value":2920,"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":324.8821459087994,"se":2.12209870563622,"t":153.0947382635519,"p":0.0,"ci_low":320.7211841324752,"ci_high":329.0431076851236},{"term":"post","coef":70.22866273024319,"se":2.233681964388368,"t":31.440761867580086,"p":3.8724964671813756e-187,"ci_low":65.84891109588257,"ci_high":74.6084143646038}]},"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":"9fd80e1dea4258fb70c3b87a7ce5c3fd8a30e1c5c2f687e103f685a5c20ab686","params_versions":[],"sources":[],"started_at":"2026-10-15T17:11:47.488386Z","duration_ms":1.7253330443054438,"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_d8f9b2ec","llm_name":"infer__ols","vendor_id":"script_1","result_for_model_ref":"3eee23f3dd4c991c2a8202d8b21ae4d0efa582ed015dcde020bb6f1185df851d"}}
{"call_id":"call_21a6b495","run_id":"20261015T171142Z-suite-causcibench_real-naive-d6494d/causcibench_real.q173/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":"2026-10-15T17:11:47.490254Z","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.post = 70,23.\", \"figure_refs\": [\"call_1eb176a1:coef.post\"], \"caveats\": []})","tokens_in":0,"tokens_out":0,"meta":{"turn_call_id":"call_21a6b495","text":"","tool_calls":[{"vendor_id":"script_2","llm_name":"submit_answer","tool_id":null,"args":{"answer":"Estimativa ingênua (infer.ols): coef.post = 70,23.","figure_refs":["call_1eb176a1:coef.post"],"caveats":[]}}],"request":{"system_stable_ref":"433a6877aa6a378bbd4b7e2752e484d8cb7499c562d68222be231f62ff2fa420","system_task_ref":"91ffee53493aa1cf9f59a1ae7c7a1ca8236f550d4de7e47450617cd289577687","tools_ref":"52ab03d8ec104abb85572294bf8a469d75c5de9e74d6042caabff8017adc572b","contents_refs":[],"messages_refs":["a97924c3d7036522ca3ad2c85b67440f09670538327744793b88ef1409929d4e","19b806d81daff3660d872a14fe3e609d8b57c0ccffe5a0d6df867f2f1b3a3614","f394d662914da668a2ef24d2bbb1376b44b91e0f13f4f2cd1b31fffb10074885"],"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":0.18824986182153225}}
{"call_id":"call_8f953d48","run_id":"20261015T171142Z-suite-causcibench_real-naive-d6494d/causcibench_real.q173/e1a1","step":"analysis","kind":"note","name":"analyst.guard","version":"","code_version":"2925aeab2262","inputs":{},"input_digest":"","dataset_digests":{},"result":null,"outputs":null,"output_digest":"","params_versions":[],"sources":[],"started_at":"2026-10-15T17:11:47.491631Z","duration_ms":0.0,"error":null,"decided_by":"code","rationale":"","cached":false,"informed_by":[],"model":null,"prompt":null,"response":null,"tokens_in":null,"tokens_out":null,"meta":{"turn_id":"call_21a6b495","numbers_found":["70,23"],"unbacked":[],"uncited":[],"invalid_refs":[],"empty":false,"verdict":"accepted","retries_left":1,"vendor_id":"script_2"}}