{"call_id":"call_098129b0","run_id":"20261015T171059Z-suite-causcibench_real-scripted-ded01d/causcibench_real.q054/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":"2026-10-15T17:11:00.764688Z","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\": \"carpenter_the_minimum_legal\",\n \"rows\": 80,\n \"description\": \"This dataset aggregates near-census administrative records of emergency department visits and inpatient hospital admissions for U.S. young adults into age-in-months bins around the 21st birthday, with outcomes expressed as rates per 10,000 person-years by cause, gender, pregnancy exclusion, and contributing state tags where available. Age-running variables index months relative to 21, along with pre/post age polynomials and an indicator for the 21st-birthday month to accommodate birthday-month spikes documented in alcohol intoxication visits.\\n\\nVariables:\\nmonths_21: Integer months relative to the 21st birthday (e.g., -24 to +24), indexing the running age variable used to organize age-specific rates\\nage: Age reported in years corresponding to the same age-month cell, provided for convenience alongside months_21\\nbmonth: Indicator equal to 1 for the month containing the 21st birthday (the \\\"\\\"birthday month\\\"\\\" control used to accommodate celebration spikes), 0 otherwise\\npost: Indicator equal to 1 for observations at or above age 21 in months (post-threshold), 0 otherwise\\nlinear, linear_post: Linear age terms for pre- and post-threshold segments, capturing smooth age trends on either side of age 21 within the specified bandwidth\\nage_months: Age in months as a non-centered index (e.g., from 228 to 276), facilitating alternative specifications and diagnostics\\nage_c, age_c_sq: Age in months centered at 21 (0 at 21) and its square for flexible smooth trends\\nover: Synonym for post (equals 1 at and after 21), supplied for alternate coding conventions in estimation\\nage_c_post, age_c_post_sq: Interactions of centered age and its square with the post indicator to allow different slopes/curvature after 21\\ndummy21: Binary flag for the 21st-birthday month, identical in purpose to bmonth for absorbing discrete celebration effects in that month\\nvisit_all_r: All non-pregnancy-screened ED visits per 10,000 person-years in the age-month cell\\nalcohol_any_all_r: ED visits with primary alcohol intoxication coding per 10,000\\ninjury_all_r: ED visits for any injury cause per 10,000\\ninj_by_self_all_r / inj_by_oth_all_r / inj_accident_all_r: ED visits per 10,000 for self-inflicted injuries, injuries inflicted by others, and accidental injuries, respectively\\nillness_all_r: ED visits per 10,000 for residual illness (non-alcohol, non-injury)\\ninjury_or_alc_all_r: Composite of injury or alcohol intoxication ED visits per 10,000; visit_all_np_r, alcohol_any_all_np_r, injury_all_np_r, inj_by_self_all_np_r, inj_by_oth_all_np_r, inj_accident_all_np_r, illness_all_np_r, injury_or_alc_all_np_r: Same definitions as \\\"\\\"all_r\\\"\\\" but excluding pregnancy-related encounters, aligning with the study's analytic sample\\nvisit_m_r, alcohol_any_m_r, injury_m_r, inj_by_self_m_r, inj_by_oth_m_r, inj_accident_m_r, illness_m_r, injury_or_alc_m_r: Male-only ED rates per 10,000 by cause\\nvisit_f_r, alcohol_any_f_r, injury_f_r, inj_by_self_f_r, inj_by_oth_f_r, inj_accident_f_r, illness_f_r, injury_or_alc_f_r: Female-only ED rates per 10,000 by cause\\nvisit_f_np_r, alcohol_any_f_np_r, injury_f_np_r, inj_by_self_f_np_r, inj_by_oth_f_np_r, inj_accident_f_np_r, illness_f_np_r, injury_or_alc_f_np_r: Female-only non-pregnancy ED rates per 10,000 by cause\\nvisit_az_np_r, alcohol_any_az_np_r, injury_az_np_r, inj_by_self_az_np_r, inj_by_oth_az_np_r, inj_accident_az_np_r, illness_az_np_r, injury_or_alc_az_np_r: Arizona-specific ED rates per 10,000 (non-pregnancy), by cause\\nvisit_ca_np_r, alcohol_any_ca_np_r, injury_ca_np_r, inj_by_self_ca_np_r, inj_by_oth_ca_np_r, inj_accident_ca_np_r, illness_ca_np_r, injury_or_alc_ca_np_r: State-tagged ED rates provided where available in replication files, analogous definitions as above\\nvisit_ia_np_r, alcohol_any_ia_np_r, injury_ia_np_r, inj_by_self_ia_np_r, inj_by_oth_ia_np_r, inj_accident_ia_np_r, illness_ia_np_r, injury_or_alc_ia_np_r: State-tagged ED rates provided where available in replication files, analogous definitions as above\\nvisit_nj_np_r, alcohol_any_nj_np_r, injury_nj_np_r, inj_by_self_nj_np_r, inj_by_oth_nj_np_r, inj_accident_nj_np_r, illness_nj_np_r, injury_or_alc_nj_np_r: New Jersey-specific ED rates per 10,000 (non-pregnancy), by cause\\nvisit_wi_np_r, alcohol_any_wi_np_r, injury_wi_np_r, inj_by_self_wi_np_r, inj_by_oth_wi_np_r, inj_accident_wi_np_r, illness_wi_np_r, injury_or_alc_wi_np_r: Wisconsin-specific ED rates per 10,000 (non-pregnancy), by cause\",\n \"columns\": {\n  \"months_21\": \"float64\",\n  \"age\": \"float64\",\n  \"bmonth\": \"float64\",\n  \"post\": \"float64\",\n  \"linear\": \"float64\",\n  \"linear_post\": \"float64\",\n  \"visit_all_r\": \"float64\",\n  \"alcohol_any_all_r\": \"float64\",\n  \"injury_all_r\": \"float64\",\n  \"inj_by_self_all_r\": \"float64\",\n  \"inj_by_oth_all_r\": \"float64\",\n  \"inj_accident_all_r\": \"float64\",\n  \"illness_all_r\": \"float64\",\n  \"injury_or_alc_all_r\": \"float64\",\n  \"visit_all_np_r\": \"float64\",\n  \"alcohol_any_all_np_r\": \"float64\",\n  \"injury_all_np_r\": \"float64\",\n  \"inj_by_self_all_np_r\": \"float64\",\n  \"inj_by_oth_all_np_r\": \"float64\",\n  \"inj_accident_all_np_r\": \"float64\",\n  \"illness_all_np_r\": \"float64\",\n  \"injury_or_alc_all_np_r\": \"float64\",\n  \"visit_m_r\": \"float64\",\n  \"alcohol_any_m_r\": \"float64\",\n  \"injury_m_r\": \"float64\",\n  \"inj_by_self_m_r\": \"float64\",\n  \"inj_by_oth_m_r\": \"float64\",\n  \"inj_accident_m_r\": \"float64\",\n  \"illness_m_r\": \"float64\",\n  \"injury_or_alc_m_r\": \"float64\",\n  \"visit_f_r\": \"float64\",\n  \"alcohol_any_f_r\": \"float64\",\n  \"injury_f_r\": \"float64\",\n  \"inj_by_self_f_r\": \"float64\",\n  \"inj_by_oth_f_r\": \"float64\",\n  \"inj_accident_f_r\": \"float64\",\n  \"illness_f_r\": \"float64\",\n  \"injury_or_alc_f_r\": \"float64\",\n  \"visit_f_np_r\": \"float64\",\n  \"alcohol_any_f_np_r\": \"float64\",\n  \"injury_f_np_r\": \"float64\",\n  \"inj_by_self_f_np_r\": \"float64\",\n  \"inj_by_oth_f_np_r\": \"float64\",\n  \"inj_accident_f_np_r\": \"float64\",\n  \"illness_f_np_r\": \"float64\",\n  \"injury_or_alc_f_np_r\": \"float64\",\n  \"visit_az_np_r\": \"float64\",\n  \"alcohol_any_az_np_r\": \"float64\",\n  \"injury_az_np_r\": \"float64\",\n  \"inj_by_self_az_np_r\": \"float64\",\n  \"inj_by_oth_az_np_r\": \"float64\",\n  \"inj_accident_az_np_r\": \"float64\",\n  \"illness_az_np_r\": \"float64\",\n  \"injury_or_alc_az_np_r\": \"float64\",\n  \"visit_ca_np_r\": \"float64\",\n  \"alcohol_any_ca_np_r\": \"float64\",\n  \"injury_ca_np_r\": \"float64\",\n  \"inj_by_self_ca_np_r\": \"float64\",\n  \"inj_by_oth_ca_np_r\": \"float64\",\n  \"inj_accident_ca_np_r\": \"float64\",\n  \"illness_ca_np_r\": \"float64\",\n  \"injury_or_alc_ca_np_r\": \"float64\",\n  \"visit_ia_np_r\": \"float64\",\n  \"alcohol_any_ia_np_r\": \"float64\",\n  \"injury_ia_np_r\": \"float64\",\n  \"inj_by_self_ia_np_r\": \"float64\",\n  \"inj_by_oth_ia_np_r\": \"float64\",\n  \"inj_accident_ia_np_r\": \"float64\",\n  \"illness_ia_np_r\": \"float64\",\n  \"injury_or_alc_ia_np_r\": \"float64\",\n  \"visit_nj_np_r\": \"float64\",\n  \"alcohol_any_nj_np_r\": \"float64\",\n  \"injury_nj_np_r\": \"float64\",\n  \"inj_by_self_nj_np_r\": \"float64\",\n  \"inj_by_oth_nj_np_r\": \"float64\",\n  \"inj_accident_nj_np_r\": \"float64\",\n  \"illness_nj_np_r\": \"float64\",\n  \"injury_or_alc_nj_np_r\": \"float64\",\n  \"visit_wi_np_r\": \"float64\",\n  \"alcohol_any_wi_np_r\": \"float64\",\n  \"injury_wi_np_r\": \"float64\",\n  \"inj_by_self_wi_np_r\": \"float64\",\n  \"inj_by_oth_wi_np_r\": \"float64\",\n  \"inj_accident_wi_np_r\": \"float64\",\n  \"illness_wi_np_r\": \"float64\",\n  \"injury_or_alc_wi_np_r\": \"float64\",\n  \"age_months\": \"float64\",\n  \"age_c\": \"float64\",\n  \"age_c_sq\": \"float64\",\n  \"over\": \"float64\",\n  \"age_c_post\": \"float64\",\n  \"age_c_post_sq\": \"float64\",\n  \"dummy21\": \"float64\"\n }\n}\n\n# Question\nDoes gaining legal access to alcohol at the U.S. minimum legal drinking age cause an increase in nonfatal morbidity?","response":"causcibench_metadata_rule/1: rdd\ninfer__ols({\"dataset\": \"carpenter_the_minimum_legal\", \"y\": \"visit_all_np_r\", \"x\": [\"over\"], \"controls\": [\"months_21\", \"dummy21\", \"age_c\", \"age_c_sq\", \"age_c_post\", \"age_c_post_sq\"]})","tokens_in":0,"tokens_out":0,"meta":{"turn_call_id":"call_098129b0","text":"causcibench_metadata_rule/1: rdd","tool_calls":[{"vendor_id":"script_1","llm_name":"infer__ols","tool_id":"infer.ols","args":{"dataset":"carpenter_the_minimum_legal","y":"visit_all_np_r","x":["over"],"controls":["months_21","dummy21","age_c","age_c_sq","age_c_post","age_c_post_sq"]}}],"request":{"system_stable_ref":"433a6877aa6a378bbd4b7e2752e484d8cb7499c562d68222be231f62ff2fa420","system_task_ref":"fa0a3d9fe0f3221026ec0e44acd4e5d28049bce4b256817b8463e2ff53717c06","tools_ref":"52ab03d8ec104abb85572294bf8a469d75c5de9e74d6042caabff8017adc572b","contents_refs":[],"messages_refs":["8d4768333f84d2b4ca34e87b6359ce21ba5bf67610dc077d2e0af279b86861d5"],"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":0.8898328524082899}}
{"call_id":"call_51b8b40b","run_id":"20261015T171059Z-suite-causcibench_real-scripted-ded01d/causcibench_real.q054/e1a1","step":"analysis","kind":"tool","name":"infer.ols","version":"0.1.0","code_version":"2925aeab2262","inputs":{"dataset":"carpenter_the_minimum_legal","y":"visit_all_np_r","x":["over"],"controls":["months_21","dummy21","age_c","age_c_sq","age_c_post","age_c_post_sq"],"fe":[],"cluster":null,"se":"HC1","weights":null,"subset":null,"intercept":true,"level":0.95},"input_digest":"230867312cec66230ca76a91101320d3160424f1bce4490a3ab621c7b736cfb1","dataset_digests":{"carpenter_the_minimum_legal":"445d2bbe7bb38003d078f082d967e07cd8383187389dffe2765cc6a38a5ce066"},"result":{"tool":"infer.ols","version":"0.1.0","call_id":"call_51b8b40b","status":"ok","reason":"","figures":[{"key":"coef.over","value":162.98920003605897,"unit":"","label":"Coefficient on over in the regression of visit_all_np_r","se":30.333516051790983,"ci_low":102.53458972670578,"ci_high":223.44381034541217,"ci_level":0.95,"derivation":{"steps":[{"text_pt":"Coeficiente de over na regressão por mínimos quadrados ordinários de visit_all_np_r.","formula":"","terms":[{"symbol":"n","value":80.0,"unit":"observações","note":""},{"symbol":"k","value":7.0,"unit":"parâmetros","note":""}],"result":162.98920003605897,"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":920.1221960642613,"unit":"","note":"diagonal da matriz de covariância"}],"result":30.333516051790983,"unit":""},{"text_pt":"Intervalo de 95% de confiança: a estimativa mais ou menos o valor crítico da distribuição t de Student com 73 graus de liberdade vezes o erro-padrão.","formula":"IC = β ± c · ep","terms":[{"symbol":"β","value":162.98920003605897,"unit":"","note":""},{"symbol":"c","value":1.992997125889855,"unit":"","note":"quantil 0.975"},{"symbol":"ep","value":30.333516051790983,"unit":"","note":""}],"result":null,"unit":""}],"note_pt":""}}],"diagnostics":[{"name":"n_obs","value":80,"flag":false,"note":"observations used"},{"name":"k","value":7,"flag":false,"note":"parameters estimated, fixed-effect dummies included"},{"name":"r2","value":0.8925098904625572,"flag":false,"note":""},{"name":"df_resid","value":73,"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":3890.1553520384923,"se":29.351548512079717,"t":132.53663091872264,"p":9.653614663231278e-89,"ci_low":3831.6578002135006,"ci_high":3948.652903863484},{"term":"over","coef":162.98920003605897,"se":30.333516051790983,"t":5.37323796416392,"p":8.837174535120515e-7,"ci_low":102.53458972670578,"ci_high":223.44381034541217},{"term":"months_21","coef":-13.193444258224348,"se":3.8620367395041506,"t":-3.4161881794833113,"p":0.0010409058489956563,"ci_low":-20.890472380137147,"ci_high":-5.49641613631155},{"term":"dummy21","coef":25.900953639721564,"se":7.655638184194235,"t":3.383252057705189,"p":0.0011547969909556186,"ci_low":10.643288741769826,"ci_high":41.158618537673306},{"term":"age_c_sq","coef":-94.72252626873892,"se":14.741223951889612,"t":-6.425689384943973,"p":1.1863728040408033e-8,"ci_low":-124.10174323695361,"ci_high":-65.34330930052423},{"term":"age_c_post","coef":95.14533625885376,"se":47.91545665897092,"t":1.985691943541986,"p":0.05082366181918291,"ci_low":-0.35003114817519076,"ci_high":190.6407036658827},{"term":"age_c_post_sq","coef":98.30170746483647,"se":15.192871287271776,"t":6.470252107459851,"p":9.838490881307685e-9,"ci_low":68.02235865528931,"ci_high":128.58105627438363}]},"datasets":{},"warnings":["dropped as collinear with the other regressors: ['age_c']"],"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":"b40e5a3e82de526f096de089208b260935a2a2573847548156520e8d85153c15","params_versions":[],"sources":[],"started_at":"2026-10-15T17:11:00.767687Z","duration_ms":3.9013330824673176,"error":null,"decided_by":"model","rationale":"causcibench_metadata_rule/1: rdd","cached":false,"informed_by":[],"model":null,"prompt":null,"response":null,"tokens_in":null,"tokens_out":null,"meta":{"turn_id":"call_098129b0","llm_name":"infer__ols","vendor_id":"script_1","result_for_model_ref":"2e5f3d8b9ba99a5e1f88aa5b355db0abeea5d1838e69cee6b2ca8408fe07a67c"}}
{"call_id":"call_54b3525f","run_id":"20261015T171059Z-suite-causcibench_real-scripted-ded01d/causcibench_real.q054/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":"2026-10-15T17:11:00.771841Z","duration_ms":0.0,"error":null,"decided_by":"model","rationale":"","cached":false,"informed_by":[],"model":"reference","prompt":null,"response":"submit_answer({\"answer\": \"Estimativa (infer.ols): coef.over = 163,0.\", \"figure_refs\": [\"call_51b8b40b:coef.over\"], \"caveats\": []})","tokens_in":0,"tokens_out":0,"meta":{"turn_call_id":"call_54b3525f","text":"","tool_calls":[{"vendor_id":"script_2","llm_name":"submit_answer","tool_id":null,"args":{"answer":"Estimativa (infer.ols): coef.over = 163,0.","figure_refs":["call_51b8b40b:coef.over"],"caveats":[]}}],"request":{"system_stable_ref":"433a6877aa6a378bbd4b7e2752e484d8cb7499c562d68222be231f62ff2fa420","system_task_ref":"fa0a3d9fe0f3221026ec0e44acd4e5d28049bce4b256817b8463e2ff53717c06","tools_ref":"52ab03d8ec104abb85572294bf8a469d75c5de9e74d6042caabff8017adc572b","contents_refs":[],"messages_refs":["8d4768333f84d2b4ca34e87b6359ce21ba5bf67610dc077d2e0af279b86861d5","0db2b487c3d6332bdcd151d50b2c770396dae747e3e7a043cdf3c0ef3e75b731","2273b867a9408198aa407a1527eebcd865750fbcadb6a0065707bbc807f0c1fa"],"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":0.3228338900953531}}
{"call_id":"call_38e6314f","run_id":"20261015T171059Z-suite-causcibench_real-scripted-ded01d/causcibench_real.q054/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":"2026-10-15T17:11:00.773239Z","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_54b3525f","numbers_found":["163,0"],"unbacked":[],"uncited":[],"invalid_refs":[],"empty":false,"verdict":"accepted","retries_left":1,"vendor_id":"script_2"}}