{"call_id":"call_ae2a7f77","run_id":"20261015T171059Z-suite-causcibench_real-scripted-ded01d/causcibench_real.q050/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":"2026-10-15T17:11:00.727867Z","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\": \"eckhouse_metrics_management\",\n \"rows\": 1085,\n \"description\": \"This dataset is an agency-year panel that links police-reported crime, clearances, and unfounded counts to population, mortality, and local political context, tracking the timing and status of metrics-focused management practices within U.S. law enforcement agencies. The unit of observation is the police agency by calendar year, identified by a standard agency code and geographic identifiers.\\n\\nThe variables are:\\nAGENCY: Agency name used in reporting\\nYEAR: Calendar year of the observation for the agency\\nORI: Agency's Originating Agency Identifier used in federal crime reporting systems\\nSTATE: State of the agency's jurisdiction\\nCOUNTY: County of the agency's jurisdiction\\nPOP: Jurisdiction population for the agency-year (denominator for rate variables)\\nCOMPSTAT: Indicator for adoption of a metrics-focused management program by the agency\\nCOMPSTATDATE: Date (or year) of the agency's program adoption as recorded in the source\\nHASCOMPSTAT: Indicator equal to 1 in years at or after adoption, and 0 otherwise\\nYEAR.UNFOUNDED.MURDER: Count of homicides deemed unfounded in the agency-year\\nYEAR.UNFOUNDED.RAPE.TOT: Count of rapes deemed unfounded in the agency-year\\nYEAR.UNFOUND.AUTO.THFT: Count of motor vehicle thefts deemed unfounded in the agency-year\\nYEAR.ACT.NUM.MURDER: Count of homicide offenses known to the agency in the year (UCR \\\"\\\"actual\\\"\\\" offenses)\\nYEAR.ACT.NUM.RAPE.TOT: Count of rape offenses known to the agency in the year\\nYEAR.ACT.NUM.ASSAULT.TOT: Count of aggravated assault offenses known to the agency in the year\\nYEAR.ACT.VHC.THFT.TOT: Count of motor vehicle theft offenses known to the agency in the year\\nYEAR.ACT.ALL.FIELDS: Total \\\"\\\"actual\\\"\\\" offenses across included categories in the agency-year\\nYEAR.TOT.CLR.MURDER: Number of homicide offenses cleared (by arrest or exceptional means) in the agency-year\\nYEAR.TOT.CLR.RAPE.TOT: Number of rape offenses cleared in the agency-year\\nYEAR.TOT.CLR.ASSAULT.TOT: Number of aggravated assaults cleared in the agency-year; YEAR.TOT.CLR.VHC.THFT.TOT: Number of motor vehicle thefts cleared in the agency-year\\nYEAR.TOT.CLR.ALL.FIELDS: Total clearances across included categories in the agency-year\\nSHAREPT2: Share of Part II arrests in total arrests (proportion)\\nclearrate: Overall clearance rate across included offense categories (clearances divided by offenses)\\nunfoundrapepct: Percent of reported rapes deemed unfounded\\nmurderclearrate: Clearance rate for homicide\\nrapeclearrate: Clearance rate for rape\\nassaultclearrate: Clearance rate for aggravated assault\\nunfoundmurderpct: Percent of reported homicides deemed unfounded\\nunfoundassaultpct: Percent of reported aggravated assaults deemed unfounded\\nPART1arrests: Count of arrests for UCR Part I offenses in the agency-year\\nPART2arrests: Count of arrests for UCR Part II offenses in the agency-year\\npopblackpct: Percent of the jurisdiction's population identified as Black/African American\\npopwhitepct: Percent of the jurisdiction's population identified as White\\nunfoundautopct: Percent of reported motor vehicle thefts deemed unfounded\\nyearssincecompstat: Years elapsed since the agency's adoption year (constructed relative to COMPSTATDATE)\\nFIPS_PLACE_ID: FIPS place identifier for the agency's primary jurisdiction\\ncdcHomicide: Cause-specific mortality measure for homicide matched to the agency's geography (level/units follow source coding)\\ncdcSuicide: Cause-specific mortality measure for suicide matched to the agency's geography (level/units follow source coding)\\ncdcUndetermined: Cause-specific mortality measure for undetermined intent matched to the agency's geography (level/units follow source coding)\\ncdcUnintentional: Cause-specific mortality measure for unintentional injury matched to the agency's geography (level/units follow source coding)\\nhaselection: Indicator equal to 1 if a relevant local election occurred for the agency's jurisdiction in that year\\nyears_since_election: Years since the most recent relevant local election for the agency's jurisdiction\\npartisantotal: Summary measure of local political partisanship for the jurisdiction (construction follows source coding)\\nincidentsperpop: Total incidents per population (offenses divided by POP), a per-capita incident rate\\nautoclearrate: Clearance rate for motor vehicle theft\\nYEAR.ACT.NUM.KILLINGS: Count of killings as coded in the source (maintained separately from homicide for internal consistency and checks)\",\n \"columns\": {\n  \"AGENCY\": \"str\",\n  \"YEAR\": \"int64\",\n  \"ORI\": \"str\",\n  \"STATE\": \"str\",\n  \"COUNTY\": \"float64\",\n  \"POP\": \"int64\",\n  \"COMPSTAT\": \"int64\",\n  \"COMPSTATDATE\": \"int64\",\n  \"HASCOMPSTAT\": \"int64\",\n  \"YEAR.UNFOUNDED.MURDER\": \"int64\",\n  \"YEAR.UNFOUNDED.RAPE.TOT\": \"int64\",\n  \"YEAR.UNFOUND.AUTO.THFT\": \"int64\",\n  \"YEAR.ACT.NUM.MURDER\": \"int64\",\n  \"YEAR.ACT.NUM.RAPE.TOT\": \"int64\",\n  \"YEAR.ACT.NUM.ASSAULT.TOT\": \"int64\",\n  \"YEAR.ACT.VHC.THFT.TOT\": \"int64\",\n  \"YEAR.ACT.ALL.FIELDS\": \"int64\",\n  \"YEAR.TOT.CLR.MURDER\": \"int64\",\n  \"YEAR.TOT.CLR.RAPE.TOT\": \"int64\",\n  \"YEAR.TOT.CLR.ASSAULT.TOT\": \"int64\",\n  \"YEAR.TOT.CLR.VHC.THFT.TOT\": \"int64\",\n  \"YEAR.TOT.CLR.ALL.FIELDS\": \"int64\",\n  \"SHAREPT2\": \"float64\",\n  \"clearrate\": \"float64\",\n  \"unfoundrapepct\": \"float64\",\n  \"murderclearrate\": \"float64\",\n  \"rapeclearrate\": \"float64\",\n  \"assaultclearrate\": \"float64\",\n  \"unfoundmurderpct\": \"float64\",\n  \"unfoundassaultpct\": \"float64\",\n  \"PART1arrests\": \"float64\",\n  \"PART2arrests\": \"float64\",\n  \"popblackpct\": \"float64\",\n  \"popwhitepct\": \"float64\",\n  \"unfoundautopct\": \"float64\",\n  \"yearssincecompstat\": \"int64\",\n  \"FIPS_PLACE_ID\": \"int64\",\n  \"cdcHomicide\": \"int64\",\n  \"cdcSuicide\": \"int64\",\n  \"cdcUndetermined\": \"float64\",\n  \"cdcUnintentional\": \"float64\",\n  \"haselection\": \"int64\",\n  \"years_since_election\": \"int64\",\n  \"partisantotal\": \"int64\",\n  \"incidentsperpop\": \"float64\",\n  \"autoclearrate\": \"float64\",\n  \"YEAR.ACT.NUM.KILLINGS\": 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{"call_id":"call_3d1b4154","run_id":"20261015T171059Z-suite-causcibench_real-scripted-ded01d/causcibench_real.q050/e1a1","step":"analysis","kind":"tool","name":"infer.ols","version":"0.1.0","code_version":"2925aeab2262","inputs":{"dataset":"eckhouse_metrics_management","y":"PART2arrests","x":["HASCOMPSTAT"],"controls":["POP"],"fe":["AGENCY","YEAR"],"cluster":null,"se":"HC1","weights":null,"subset":null,"intercept":true,"level":0.95},"input_digest":"c8c4f5a3cdff32f0e655806d5a5c93282cabf7b80e023c9f881e809204a1db24","dataset_digests":{"eckhouse_metrics_management":"edcc0e3e260047813f052e191a75de4b0bf34c47415a9adb49cf78c6244589b6"},"result":{"tool":"infer.ols","version":"0.1.0","call_id":"call_3d1b4154","status":"ok","reason":"","figures":[{"key":"coef.HASCOMPSTAT","value":20471.05741625657,"unit":"","label":"Coefficient on HASCOMPSTAT in the regression of PART2arrests","se":6658.553954809597,"ci_low":7404.857998177922,"ci_high":33537.25683433522,"ci_level":0.95,"derivation":{"steps":[{"text_pt":"Coeficiente de HASCOMPSTAT na regressão por mínimos quadrados ordinários de PART2arrests, efeitos fixos de AGENCY, YEAR.","formula":"","terms":[{"symbol":"n","value":1081.0,"unit":"observações","note":""},{"symbol":"k","value":72.0,"unit":"parâmetros","note":""}],"result":20471.05741625657,"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":44336340.76911052,"unit":"","note":"diagonal da matriz de covariância"}],"result":6658.553954809597,"unit":""},{"text_pt":"Intervalo de 95% de confiança: a estimativa mais ou menos o valor crítico da distribuição t de Student com 1009 graus de liberdade vezes o erro-padrão.","formula":"IC = β ± c · ep","terms":[{"symbol":"β","value":20471.05741625657,"unit":"","note":""},{"symbol":"c","value":1.9623178706302578,"unit":"","note":"quantil 0.975"},{"symbol":"ep","value":6658.553954809597,"unit":"","note":""}],"result":null,"unit":""}],"note_pt":""}}],"diagnostics":[{"name":"n_obs","value":1081,"flag":false,"note":"observations used"},{"name":"k","value":72,"flag":false,"note":"parameters estimated, fixed-effect dummies included"},{"name":"r2","value":0.6300993423273054,"flag":false,"note":""},{"name":"df_resid","value":1009,"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"},{"name":"fixed_effects","value":"AGENCY (47 levels), YEAR (24 levels)","flag":false,"note":"absorbed as dummies; their coefficients are not tabulated"}],"tables":{"coefficients":[{"term":"Intercept","coef":132965.03523780912,"se":26501.4617543785,"t":5.017271744108266,"p":6.191006261001499e-7,"ci_low":80960.74323936789,"ci_high":184969.32723625033},{"term":"HASCOMPSTAT","coef":20471.05741625657,"se":6658.553954809597,"t":3.0743998704808795,"p":0.0021659751936975303,"ci_low":7404.857998177922,"ci_high":33537.25683433522},{"term":"POP","coef":-0.30381783387416816,"se":0.06432138814211119,"t":-4.72343403414919,"p":2.6469661928991627e-6,"ci_low":-0.43003684328917813,"ci_high":-0.17759882445915823}]},"datasets":{},"warnings":["4 rows were dropped because one of 'PART2arrests', 'HASCOMPSTAT', 'POP', 'AGENCY', 'YEAR' was missing"],"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":"6d16b818dcd5d6860d8d1b4c8208a40b4b5ae73fd11d104b315ee1598a18073d","params_versions":[],"sources":[],"started_at":"2026-10-15T17:11:00.730910Z","duration_ms":11.170790996402502,"error":null,"decided_by":"model","rationale":"causcibench_metadata_rule/1: did","cached":false,"informed_by":[],"model":null,"prompt":null,"response":null,"tokens_in":null,"tokens_out":null,"meta":{"turn_id":"call_ae2a7f77","llm_name":"infer__ols","vendor_id":"script_1","result_for_model_ref":"09c556a32a0dd975829084c7b947d463255ce7d35b822ba73002c0764b8d44b2"}}
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