{"call_id":"call_84727e7d","run_id":"20261015T171142Z-suite-causcibench_real-naive-d6494d/causcibench_real.q056/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":"2026-10-15T17:11:43.994935Z","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\": \"dhingra_immigration_policies\",\n \"rows\": 18816,\n \"description\": \"This dataset is a county-year panel for U.S. counties that combines FBI Uniform Crime Reporting (UCR) crime indices with American Community Survey (ACS) demographics and local socio-economic covariates, structured to cover multiple years with explicit indicators for 2013-2017.\\n\\nVariables:\\nyear: Calendar year of the county observation\\nstate_abb: Two-letter state abbreviation for the county's state\\nfips_state_county: Five-digit FIPS code uniquely identifying the county (state FIPS + county FIPS)\\ncounty_name: Descriptive county name (e.g., \\\"\\\"Travis County\\\"\\\")\\nYear2013, Year2014, Year2015, Year2016, Year2017: Year indicator variables equal to 1 in the corresponding year and 0 otherwise\\nViolentCrimeIndex: Reported violent crimes per 100,000 residents (homicide, rape, robbery, aggravated assault), following UCR definitions\\nPropertyCrimeIndex: Reported property crimes per 100,000 residents (burglary, motor-vehicle theft, larceny-theft, arson), following UCR definitions\\nTotalCrimeIndex: Sum of violent and property crime indices per 100,000 residents\\ncoverage_indicator: Data completeness flag for UCR reporting within the county-year (e.g., indicates full reporting coverage by local agencies in that year)\\nCountyPopulation_ACS: Resident population from ACS 5-year estimates corresponding to the county-year\\nHispanicPct: Share of county residents identifying as Hispanic/Latino (percent)\\nBlackPopPct: Share of county residents identifying as Black/African American (percent)\\nWhitePct: Share of county residents identifying as White (percent)\\nAsianPct: Share of county residents identifying as Asian (percent)\\nCountyPopDens: Population density of the county (residents per unit area, consistent with ACS/county geography inputs)\\nMale15_34PopPct: Share of county population that is male and ages 15-34 (percent)\\nSingleMotherHHs: Count or rate of single-mother households in the county (as compiled from ACS family/household tables)\\nUnemploymentRate: County unemployment rate (percent) from ACS labor force statistics\\nManufacturingIndustryPct: Share of employed residents working in manufacturing (percent)\\nRetailHospitalityIndustryPct: Share of employed residents working in retail trade and accommodation/food services (percent)\\nRenterPopulationPct: Share of occupied housing units that are renter-occupied (percent)\\nMedianHomeValue: Median value of owner-occupied housing units (inflation in the ACS reference period)\\nPctHousingUnitsNoVehicle: Share of occupied housing units with no available vehicle (percent)\",\n \"columns\": {\n  \"year\": \"float64\",\n  \"state_abb\": \"str\",\n  \"fips_state_county\": \"int64\",\n  \"county_name\": \"str\",\n  \"ViolentCrimeIndex\": \"float64\",\n  \"PropertyCrimeIndex\": \"float64\",\n  \"HispanicPct\": \"float64\",\n  \"CountyPopulation_ACS\": \"float64\",\n  \"Year2017\": \"float64\",\n  \"Year2016\": \"float64\",\n  \"Year2015\": \"float64\",\n  \"Year2014\": \"float64\",\n  \"Year2013\": \"float64\",\n  \"BlackPopPct\": \"float64\",\n  \"WhitePct\": \"float64\",\n  \"AsianPct\": \"float64\",\n  \"TotalCrimeIndex\": \"float64\",\n  \"CountyPopDens\": \"float64\",\n  \"Male15_34PopPct\": \"float64\",\n  \"SingleMotherHHs\": \"float64\",\n  \"UnemploymentRate\": \"float64\",\n  \"ManufacturingIndustryPct\": \"float64\",\n  \"RetailHospitalityIndustryPct\": \"float64\",\n  \"RenterPopulationPct\": \"float64\",\n  \"MedianHomeValue\": \"float64\",\n  \"PctHousingUnitsNoVehicle\": \"float64\",\n  \"coverage_indicator\": \"float64\"\n }\n}\n\n# Question\nWhat is the effect of a country's Hispanic population share on the change in total reported crime in 2017?","response":"submit_answer({\"answer\": \"Linha de base ingênua: o item não declara o resultado e o tratamento, então não há regressão ingênua a fazer e nenhuma estimativa é citada.\", \"figure_refs\": [], \"caveats\": []})","tokens_in":0,"tokens_out":0,"meta":{"turn_call_id":"call_84727e7d","text":"","tool_calls":[{"vendor_id":"script_1","llm_name":"submit_answer","tool_id":null,"args":{"answer":"Linha de base ingênua: o item não declara o resultado e o tratamento, então não há regressão ingênua a fazer e nenhuma estimativa é 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