{"attempt":1,"datasets":{"carpenter_the_minimum_legal":{"description_shown":"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","file_sha256":"e49a7ad686067a4e4e119b7ab85a0dc1a8e562b1b5fc83d3f4e1d9b4b626d6e4","frame_sha256":null,"role":"agent","transforms":[]}},"epoch":1,"event":"sample_init","question":"Does gaining legal access to alcohol at the U.S. minimum legal drinking age cause an increase in nonfatal morbidity?","question_sha256":"757d884e6fbb26947fc783919b67e9d692f846a9fece05cce56f547a5829327b","run_id":"20261015T171142Z-suite-causcibench_real-naive-d6494d","seq":0,"skills":[],"task_id":"causcibench_real.q054","task_sha256":"0d823c3b2a514ffe2385ecd8b304d6a6a906bbe7f25bda6cfea82de454223d77","timestamp":"2026-10-15T17:11:43.776445Z"}
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{"arguments":{"cluster":null,"controls":[],"dataset":"carpenter_the_minimum_legal","fe":[],"intercept":true,"level":0.95,"se":"HC1","subset":null,"weights":null,"x":["over"],"y":"visit_all_np_r"},"attempt":1,"cached":false,"call_id":"call_91d141a3","card_version":"0.1.0","code_version":"2925aeab2262","dataset_digests":{"carpenter_the_minimum_legal":"445d2bbe7bb38003d078f082d967e07cd8383187389dffe2765cc6a38a5ce066"},"decided_by":"model","duration_ms":2.7115000411868095,"epoch":1,"error":null,"event":"tool","informed_by":[],"input_digest":"2f142c8bab8b6cf32acd3026d4db9f77430c2a36c69b23845707c794aa6f753c","llm_name":"infer__ols","output_digest":"e8946d6e78a497a03ef1992719583a256fc7784923167503fda70662975619c3","rationale":"Linha de base ing\u00eanua: regress\u00e3o do resultado no tratamento, sem controles.","replay_class":"replayed","result":{"assumptions":["the conditional mean of the outcome is linear in the regressors","errors are independent across observations; their variance may differ"],"call_id":"call_91d141a3","datasets":{},"diagnostics":[{"flag":false,"name":"n_obs","note":"observations used","value":80},{"flag":false,"name":"k","note":"parameters estimated, fixed-effect dummies included","value":2},{"flag":false,"name":"r2","note":"","value":0.2602583958540534},{"flag":false,"name":"df_resid","note":"","value":78},{"flag":false,"name":"se_type","note":"heteroskedasticity-robust standard errors (HC1)","value":"HC1"},{"flag":false,"name":"n_clusters","note":"errors not clustered","value":null}],"figures":[{"ci_high":230.96761142517397,"ci_level":0.95,"ci_low":103.75858065035527,"derivation":{"note_pt":"","steps":[{"formula":"","result":167.36309603776462,"terms":[{"note":"","symbol":"n","unit":"observa\u00e7\u00f5es","value":80.0},{"note":"","symbol":"k","unit":"par\u00e2metros","value":2.0}],"text_pt":"Coeficiente de over na regress\u00e3o por m\u00ednimos quadrados ordin\u00e1rios de visit_all_np_r.","unit":""},{"formula":"ep = \u221aV_jj","result":31.948468761778884,"terms":[{"note":"diagonal da matriz de covari\u00e2ncia","symbol":"V_jj","unit":"","value":1020.7046562223611}],"text_pt":"Erro-padr\u00e3o robusto \u00e0 heterocedasticidade (HC1): a raiz da vari\u00e2ncia estimada do coeficiente.","unit":""},{"formula":"IC = \u03b2 \u00b1 c \u00b7 ep","result":null,"terms":[{"note":"","symbol":"\u03b2","unit":"","value":167.36309603776462},{"note":"quantil 0.975","symbol":"c","unit":"","value":1.9908470688116908},{"note":"","symbol":"ep","unit":"","value":31.948468761778884}],"text_pt":"Intervalo de 95% de confian\u00e7a: a estimativa mais ou menos o valor cr\u00edtico da distribui\u00e7\u00e3o t de Student com 78 graus de liberdade vezes o erro-padr\u00e3o.","unit":""}]},"key":"coef.over","label":"Coefficient on over in the regression of visit_all_np_r","se":31.948468761778884,"unit":"","value":167.36309603776462}],"notes":"","reason":"","status":"ok","tables":{"coefficients":[{"ci_high":3857.6714971427564,"ci_low":3735.3910008304956,"coef":3796.531248986626,"p":3.002079101738934e-91,"se":30.710670404545056,"t":123.62254548584373,"term":"Intercept"},{"ci_high":230.96761142517397,"ci_low":103.75858065035527,"coef":167.36309603776462,"p":1.3385667836599855e-06,"se":31.948468761778884,"t":5.238532628455332,"term":"over"}]},"tool":"infer.ols","version":"0.1.0","warnings":[]},"result_for_model_ref":"b01f705eb299ebf9b03079c1309a583cdebea5d543eb764a7c4b528f0a273d1f","result_sha256":"3a91560616857171ae3cb91da2df1064b8d38445ed24a4ac1e6aa9a4ae375431","run_id":"20261015T171142Z-suite-causcibench_real-naive-d6494d","seq":2,"synthetic":false,"task_id":"causcibench_real.q054","timestamp":"2026-10-15T17:11:43.779573Z","tool_id":"infer.ols","turn_id":"call_e24cc9db"}
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