{"call_id":"call_895cc3e2","run_id":"20261015T171142Z-suite-causcibench_real-naive-d6494d/causcibench_real.q018/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":"2026-10-15T17:11:43.174876Z","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\": \"cheng_does_strengthening\",\n \"rows\": 550,\n \"description\": \"This dataset examines the impact of castle-doctrine statutes on violent crime rates in the United States. It combines state-level crime data from the FBI Uniform Crime Reports (UCR) Summary Files with information on the timing of castle-doctrine law implementation across states. The laws were adopted at different times, with 21 states enacting them between 2000 and 2010.\\n\\nDataset Variables:\\nyear: Calendar year\\npost: Indicator for post-treatment period\\nsid: State ID\\nrobbery_gun_r: Region-quarter fixed effect (robbery with gun)\\njhcitizen_c: Justifiable homicides by private citizens\\njhpolice_c: Justifiable homicides by police officers\\nhomicide: Homicide rate per 100,000 state population\\nrobbery: Robbery rate per 100,000 state population\\nassault: Aggravated assault rate per 100,000 state population\\nburglary: Burglary rate per 100,000 state population\\nlarceny: Larceny rate per 100,000 state population\\nmotor: Motor vehicle theft rate per 100,000 state population\\nmurder: Murder rate per 100,000 state population\\nl_homicide: Log of homicide rate\\nl_robbery: Log of robbery rate\\nl_assault: Log of aggravated assault rate\\nl_burglary: Log of burglary rate\\nl_larceny: Log of larceny rate\\nl_motor: Log of motor vehicle theft rate\\nl_murder: Log of murder rate\\nunemployrt: State unemployment rate\\nblackm_15_24: Percent of Black males aged 15 to 24\\nwhitem_15_24: Percent of White males aged 15 to 24\\nblackm_25_44: Percent of Black males aged 25 to 44\\nwhitem_25_44: Percent of White males aged 25 to 44\\npoverty: State poverty rate\\nl_police: Logged police presence\\nl_income: Logged income\\nl_prisoner: Logged number of prisoners\\nl_lagprisoner: Lagged log prisoners\\nl_exp_subsidy: Logged subsidy spending\\nl_exp_pubwelfare: Logged public welfare spending\\nlead1, lead2, lead3, lead4, lead5, lead6, lead7, lead8, lead9, lag0, lag1, lag2, lag3, lag4, lag5: Indicators of how many time periods until/since treatment\\npopwt: Population weight\\nr20001, r20002, r20003, r20004, r20011, r20012, r20013, r20014, r20021, r20022, r20023, r20024, r20031, r20032, r20033: Region-quarter fixed effects\\ntrend_1, trend_10, trend_11, trend_12, trend_13, trend_14, trend_15, trend_16, trend_17, trend_18, trend_19: linear time trends\",\n \"columns\": {\n  \"state\": \"str\",\n  \"year\": \"int64\",\n  \"sid\": \"int64\",\n  \"cdl\": \"float64\",\n  \"pre2_cdl\": \"float64\",\n  \"caselaw\": \"float64\",\n  \"anywhere\": \"int64\",\n  \"assumption\": \"int64\",\n  \"civil\": \"int64\",\n  \"homicide_c\": \"int64\",\n  \"robbery_gun_r\": \"float64\",\n  \"jhcitizen_c\": \"int64\",\n  \"jhpolice_c\": \"int64\",\n  \"homicide\": \"float64\",\n  \"robbery\": \"float64\",\n  \"assault\": \"float64\",\n  \"burglary\": \"float64\",\n  \"larceny\": \"float64\",\n  \"motor\": \"float64\",\n  \"murder\": \"float64\",\n  \"hc_felonywsus\": \"float64\",\n  \"jhcitizen\": \"float64\",\n  \"jhpolice\": \"float64\",\n  \"population\": \"int64\",\n  \"police\": \"float64\",\n  \"unemployrt\": \"float64\",\n  \"income\": \"int64\",\n  \"blackm_15_24\": \"float64\",\n  \"whitem_15_24\": \"float64\",\n  \"blackm_25_44\": \"float64\",\n  \"whitem_25_44\": \"float64\",\n  \"prisoner\": \"float64\",\n  \"lagprisoner\": \"float64\",\n  \"poverty\": \"float64\",\n  \"exp_subsidy\": \"float64\",\n  \"exp_pubwelfare\": \"float64\",\n  \"northeast\": \"int64\",\n  \"midwest\": \"int64\",\n  \"south\": \"int64\",\n  \"west\": \"int64\",\n  \"effyear\": \"float64\",\n  \"r20001\": \"float64\",\n  \"r20002\": \"float64\",\n  \"r20003\": \"float64\",\n  \"r20004\": \"float64\",\n  \"r20011\": \"float64\",\n  \"r20012\": \"float64\",\n  \"r20013\": \"float64\",\n  \"r20014\": \"float64\",\n  \"r20021\": \"float64\",\n  \"r20022\": \"float64\",\n  \"r20023\": \"float64\",\n  \"r20024\": \"float64\",\n  \"r20031\": \"float64\",\n  \"r20032\": \"float64\",\n  \"r20033\": \"float64\",\n  \"r20034\": \"float64\",\n  \"r20041\": \"float64\",\n  \"r20042\": \"float64\",\n  \"r20043\": \"float64\",\n  \"r20044\": \"float64\",\n  \"r20051\": \"float64\",\n  \"r20052\": \"float64\",\n  \"r20053\": \"float64\",\n  \"r20054\": \"float64\",\n  \"r20061\": \"float64\",\n  \"r20062\": \"float64\",\n  \"r20063\": \"float64\",\n  \"r20064\": \"float64\",\n  \"r20071\": \"float64\",\n  \"r20072\": \"float64\",\n  \"r20073\": \"float64\",\n  \"r20074\": \"float64\",\n  \"r20081\": \"float64\",\n  \"r20082\": \"float64\",\n  \"r20083\": \"float64\",\n  \"r20084\": \"float64\",\n  \"r20091\": \"float64\",\n  \"r20092\": \"float64\",\n  \"r20093\": \"float64\",\n  \"r20094\": \"float64\",\n  \"r20101\": \"float64\",\n  \"r20102\": \"float64\",\n  \"r20103\": \"float64\",\n  \"r20104\": \"float64\",\n  \"trend_1\": \"float64\",\n  \"trend_2\": \"float64\",\n  \"trend_3\": \"float64\",\n  \"trend_4\": \"float64\",\n  \"trend_5\": \"float64\",\n  \"trend_6\": \"float64\",\n  \"trend_7\": \"float64\",\n  \"trend_8\": \"float64\",\n  \"trend_9\": \"float64\",\n  \"trend_10\": \"float64\",\n  \"trend_11\": \"float64\",\n  \"trend_12\": \"float64\",\n  \"trend_13\": \"float64\",\n  \"trend_14\": \"float64\",\n  \"trend_15\": \"float64\",\n  \"trend_16\": \"float64\",\n  \"trend_17\": \"float64\",\n  \"trend_18\": \"float64\",\n  \"trend_19\": \"float64\",\n  \"trend_20\": \"float64\",\n  \"trend_21\": \"float64\",\n  \"trend_22\": \"float64\",\n  \"trend_23\": \"float64\",\n  \"trend_24\": \"float64\",\n  \"trend_25\": \"float64\",\n  \"trend_26\": \"float64\",\n  \"trend_27\": \"float64\",\n  \"trend_28\": \"float64\",\n  \"trend_29\": \"float64\",\n  \"trend_30\": \"float64\",\n  \"trend_31\": \"float64\",\n  \"trend_32\": \"float64\",\n  \"trend_33\": \"float64\",\n  \"trend_34\": \"float64\",\n  \"trend_35\": \"float64\",\n  \"trend_36\": \"float64\",\n  \"trend_37\": \"float64\",\n  \"trend_38\": \"float64\",\n  \"trend_39\": \"float64\",\n  \"trend_40\": \"float64\",\n  \"trend_41\": \"float64\",\n  \"trend_42\": \"float64\",\n  \"trend_43\": \"float64\",\n  \"trend_44\": \"float64\",\n  \"trend_45\": \"float64\",\n  \"trend_46\": \"float64\",\n  \"trend_47\": \"float64\",\n  \"trend_48\": \"float64\",\n  \"trend_49\": \"float64\",\n  \"trend_50\": \"float64\",\n  \"trend_51\": \"float64\",\n  \"l_murder\": \"float64\",\n  \"l_homicide\": \"float64\",\n  \"l_homicide_c\": \"float64\",\n  \"l_robbery\": \"float64\",\n  \"l_assault\": \"float64\",\n  \"l_burglary\": \"float64\",\n  \"l_larceny\": \"float64\",\n  \"l_motor\": \"float64\",\n  \"l_robbery_gun\": \"float64\",\n  \"l_jhcitizen\": \"float64\",\n  \"l_jhpolice\": \"float64\",\n  \"l_hc_felonywsus\": \"float64\",\n  \"l_robbery_gun_r\": \"float64\",\n  \"l_pop\": \"float64\",\n  \"l_police\": \"float64\",\n  \"l_income\": \"float64\",\n  \"l_prisoner\": \"float64\",\n  \"l_lagprisoner\": \"float64\",\n  \"l_exp_subsidy\": \"float64\",\n  \"l_exp_pubwelfare\": \"float64\",\n  \"treatment_date\": \"float64\",\n  \"post\": \"float64\",\n  \"time_til\": \"float64\",\n  \"lead1\": \"float64\",\n  \"lead2\": \"float64\",\n  \"lead3\": \"float64\",\n  \"lead4\": \"float64\",\n  \"lead5\": \"float64\",\n  \"lead6\": \"float64\",\n  \"lead7\": \"float64\",\n  \"lead8\": \"float64\",\n  \"lead9\": \"float64\",\n  \"lag0\": \"float64\",\n  \"lag1\": \"float64\",\n  \"lag2\": \"float64\",\n  \"lag3\": \"float64\",\n  \"lag4\": \"float64\",\n  \"lag5\": \"float64\",\n  \"_Iyear_2001\": \"int64\",\n  \"_Iyear_2002\": \"int64\",\n  \"_Iyear_2003\": \"int64\",\n  \"_Iyear_2004\": \"int64\",\n  \"_Iyear_2005\": \"int64\",\n  \"_Iyear_2006\": \"int64\",\n  \"_Iyear_2007\": \"int64\",\n  \"_Iyear_2008\": \"int64\",\n  \"_Iyear_2009\": \"int64\",\n  \"_Iyear_2010\": \"int64\",\n  \"popwt\": \"float64\"\n }\n}\n\n# Question\nWhat is the effect of castle doctrine laws on (log) burglary?","response":"Linha de base ingênua: regressão do resultado no tratamento, sem controles.\ninfer__ols({\"dataset\": \"cheng_does_strengthening\", \"y\": \"l_burglary\", \"x\": [\"cdl\"]})","tokens_in":0,"tokens_out":0,"meta":{"turn_call_id":"call_895cc3e2","text":"Linha de base ingênua: regressão do resultado no tratamento, sem 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{"call_id":"call_21f162df","run_id":"20261015T171142Z-suite-causcibench_real-naive-d6494d/causcibench_real.q018/e1a1","step":"analysis","kind":"tool","name":"infer.ols","version":"0.1.0","code_version":"2925aeab2262","inputs":{"dataset":"cheng_does_strengthening","y":"l_burglary","x":["cdl"],"controls":[],"fe":[],"cluster":null,"se":"HC1","weights":null,"subset":null,"intercept":true,"level":0.95},"input_digest":"e469458b65168b51f6064a08886dd680de1a9d218a2bfc491c60777a0de57c5b","dataset_digests":{"cheng_does_strengthening":"1138e3007dbd4dbbf767590fa8e58b9a3e35a279edeb3fab5066342fdf58bb77"},"result":{"tool":"infer.ols","version":"0.1.0","call_id":"call_21f162df","status":"ok","reason":"","figures":[{"key":"coef.cdl","value":0.17884035127575243,"unit":"","label":"Coefficient on cdl in the regression of l_burglary","se":0.04243374306781983,"ci_low":0.09548764958189881,"ci_high":0.26219305296960604,"ci_level":0.95,"derivation":{"steps":[{"text_pt":"Coeficiente de cdl na regressão por mínimos quadrados ordinários de l_burglary.","formula":"","terms":[{"symbol":"n","value":550.0,"unit":"observações","note":""},{"symbol":"k","value":2.0,"unit":"parâmetros","note":""}],"result":0.17884035127575243,"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":0.0018006225507457474,"unit":"","note":"diagonal da matriz de covariância"}],"result":0.04243374306781983,"unit":""},{"text_pt":"Intervalo de 95% de confiança: a estimativa mais ou menos o valor crítico da distribuição t de Student com 548 graus de liberdade vezes o erro-padrão.","formula":"IC = β ± c · ep","terms":[{"symbol":"β","value":0.17884035127575243,"unit":"","note":""},{"symbol":"c","value":1.96430236099217,"unit":"","note":"quantil 0.975"},{"symbol":"ep","value":0.04243374306781983,"unit":"","note":""}],"result":null,"unit":""}],"note_pt":""}}],"diagnostics":[{"name":"n_obs","value":550,"flag":false,"note":"observations used"},{"name":"k","value":2,"flag":false,"note":"parameters estimated, fixed-effect dummies included"},{"name":"r2","value":0.032010078241582196,"flag":false,"note":""},{"name":"df_resid","value":548,"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":6.479168321825248,"se":0.01589306426532933,"t":407.67269380263775,"p":0.0,"ci_low":6.447949538165461,"ci_high":6.510387105485035},{"term":"cdl","coef":0.17884035127575243,"se":0.04243374306781983,"t":4.214578737254463,"p":0.00002926505146592184,"ci_low":0.09548764958189881,"ci_high":0.26219305296960604}]},"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":"4b68fdf5ff1d254d392373b8a8e528db7a108d82e63e84c9d2678515fa408e3f","params_versions":[],"sources":[],"started_at":"2026-10-15T17:11:43.177352Z","duration_ms":2.3761671036481857,"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_895cc3e2","llm_name":"infer__ols","vendor_id":"script_1","result_for_model_ref":"70f3acc385c99e93ac97a74a7ddf07fea621925897401dbec70d47621a218496"}}
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