{"call_id":"call_9f63899b","run_id":"20261015T171142Z-suite-causcibench_real-naive-d6494d/causcibench_real.q055/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":"2026-10-15T17:11:43.813589Z","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\": \"thompson_how_partisan\",\n \"rows\": 4044,\n \"description\": \"This dataset assembles county-year measures of sheriff cooperation with federal immigration enforcement, linked to partisan sheriff election records and policy snapshots, with outcomes normalized by population or foreign-born counts and policy flags for specific cooperation practices in 2015 and 2018. Core sources include TRAC's detainer records, ICE reports on background checks and removals, an Immigrant Legal Resource Center (ILRC) policy survey, and compiled lists of 2018 287(g) participants and sanctuary sheriffs, all aggregated to the county level for analysis.\\n\\nVariables:\\nyear, policy_year: Calendar year for outcomes; policy_year tags policy snapshots (e.g., ILRC 2015 policies and 2018 enforcement scale) used in the dataset\\nshare_detained_sheriff: Compliance rate = detained/requested for sheriff-run facilities in the county-year; missing when no requests occur\\nshare_detained_crime0, share_detained_crime1, share_detained_crime2, share_detained_crime3: Compliance shares restricted to individuals with no conviction, misdemeanor, non-aggravated felony, and aggravated felony, respectively, based on TRAC severity groupings\\nno_287g, no_detention_contract, no_ice_alerts, ice_interrogation_limits: Binary indicators from ILRC's 2015 policy inventory for the absence of a 287(g) agreement, absence of ICE detention contracts, no routine alerts to ICE about releases, and limits on ICE questioning in county jails\\nsanctuary_2018, in_287g_2018: 2018 policy flags from ICE archival listings (287(g) participation) and FAIR's sanctuary identifications at the sheriff/county level\\nenforcement_scale_2018: Combined policy scale in 2018 taking values -1 (sanctuary), 0 (neither), +1 (287(g)), summarizing county-level enforcement posture\\nlarge_county: Indicator for counties above the 100,000 population threshold (2000 Census), used to denote the high-population subsample emphasized in robustness checks\\ncounty_id, election_id: Stable identifiers linking county-year records to sheriff election records and grouping observations by the election that seated the incumbent sheriff\\nrv, rv2, rv3, rv4, rv5: Centered two-party Democratic vote share in the most recent sheriff election (Dem share minus 50) and its higher-order powers for flexible functional control; supplied as ready-to-use analysis design covariates\\ntreat: Indicator that the sitting sheriff is a Democrat for the election cycle in force in the county-year; treat_rv, treat_rv2, treat_rv3 are interactions for flexible post-win slope adjustments in modeling\\nall_sheriff_per1k_fb, all_sheriff_per1k_pop, log_all_sheriff: Total detainer requests to sheriff facilities per 1,000 foreign-born, per 1,000 residents, and log(requests+1), respectively, capturing ICE request intensity normalized by local exposure\\ndetained_sheriff_per1k_fb, detained_sheriff_per1k_pop, log_detained_sheriff: Total sheriff-facilitated detentions per 1,000 foreign-born, per 1,000 residents, and log(detentions+1), respectively\\nsub_per_fb: FBI background check submissions associated with the county's jail intake per 1,000 foreign-born, based on ICE reporting for Secure Communities/Priority Enforcement periods\\nrem_per1k_fb: Removals/deportations associated with jail-identified individuals per 1,000 foreign-born, from ICE progress reports\\nmiss_share_detained_sheriff: Indicator that the compliance rate is undefined due to zero detainer requests in the county-year, allowing explicit missingness tracking for downstream analysis; pre_share_detained_sheriff, pre_all_sheriff_per1k_fb, pre_all_sheriff_per1k_pop, pre_log_all_sheriff, pre_detained_sheriff_per1k_fb, pre_detained_sheriff_per1k_pop, pre_log_detained_sheriff, pre_year: Baseline (pre-period) versions of the corresponding outcomes anchored to pre_year for adjustment and diagnostics, aligned with lag constructions described in the study materials\\\"\",\n \"columns\": {\n  \"year\": \"int64\",\n  \"policy_year\": \"float64\",\n  \"share_detained_sheriff\": \"float64\",\n  \"share_detained_crime0\": \"float64\",\n  \"share_detained_crime1\": \"float64\",\n  \"share_detained_crime2\": \"float64\",\n  \"share_detained_crime3\": \"float64\",\n  \"no_287g\": \"float64\",\n  \"no_detention_contract\": \"float64\",\n  \"no_ice_alerts\": \"float64\",\n  \"ice_interrogation_limits\": \"float64\",\n  \"sanctuary_2018\": \"float64\",\n  \"in_287g_2018\": \"float64\",\n  \"enforcement_scale_2018\": \"float64\",\n  \"large_county\": \"float64\",\n  \"county_id\": \"float64\",\n  \"election_id\": \"float64\",\n  \"rv\": \"float64\",\n  \"treat\": \"float64\",\n  \"rv2\": \"float64\",\n  \"rv3\": \"float64\",\n  \"rv4\": \"float64\",\n  \"rv5\": \"float64\",\n  \"treat_rv\": \"float64\",\n  \"treat_rv2\": \"float64\",\n  \"treat_rv3\": \"float64\",\n  \"all_sheriff_per1k_fb\": \"float64\",\n  \"all_sheriff_per1k_pop\": \"float64\",\n  \"log_all_sheriff\": \"float64\",\n  \"detained_sheriff_per1k_fb\": \"float64\",\n  \"detained_sheriff_per1k_pop\": \"float64\",\n  \"log_detained_sheriff\": \"float64\",\n  \"sub_per_fb\": \"float64\",\n  \"rem_per1k_fb\": \"float64\",\n  \"miss_share_detained_sheriff\": \"float64\",\n  \"pre_share_detained_sheriff\": \"float64\",\n  \"pre_all_sheriff_per1k_fb\": \"float64\",\n  \"pre_all_sheriff_per1k_pop\": \"float64\",\n  \"pre_log_all_sheriff\": \"float64\",\n  \"pre_detained_sheriff_per1k_fb\": \"float64\",\n  \"pre_detained_sheriff_per1k_pop\": \"float64\",\n  \"pre_log_detained_sheriff\": \"float64\",\n  \"pre_year\": \"float64\"\n }\n}\n\n# Question\nWhat is the effect of electing a Democratic on cooperation with federal immigration authorities?","response":"Linha de base ingênua: regressão do resultado no tratamento, sem controles.\ninfer__ols({\"dataset\": \"thompson_how_partisan\", \"y\": \"share_detained_sheriff\", \"x\": [\"treat\"]})","tokens_in":0,"tokens_out":0,"meta":{"turn_call_id":"call_9f63899b","text":"Linha de base ingênua: regressão do resultado no tratamento, sem controles.","tool_calls":[{"vendor_id":"script_1","llm_name":"infer__ols","tool_id":"infer.ols","args":{"dataset":"thompson_how_partisan","y":"share_detained_sheriff","x":["treat"]}}],"request":{"system_stable_ref":"433a6877aa6a378bbd4b7e2752e484d8cb7499c562d68222be231f62ff2fa420","system_task_ref":"4df2dbf1dafbada4ce4fc56a6681ced35c9a25e346381acffda8315d62c762f2","tools_ref":"52ab03d8ec104abb85572294bf8a469d75c5de9e74d6042caabff8017adc572b","contents_refs":[],"messages_refs":["6e1846ae9cad939dee7ec72535ef5faa9d0d961c2a197c6ba8dd9d85142a9b0c"],"config":{}},"attempts":[],"response_id":null,"finish_reason":"tool_use","model_reported":"naive-ols","usage":{"input":0,"output":0,"thoughts":0,"cached":0},"thoughts":null,"vendor":false,"wall_ms":0.9726658463478088}}
{"call_id":"call_3633dc63","run_id":"20261015T171142Z-suite-causcibench_real-naive-d6494d/causcibench_real.q055/e1a1","step":"analysis","kind":"tool","name":"infer.ols","version":"0.1.0","code_version":"2925aeab2262","inputs":{"dataset":"thompson_how_partisan","y":"share_detained_sheriff","x":["treat"],"controls":[],"fe":[],"cluster":null,"se":"HC1","weights":null,"subset":null,"intercept":true,"level":0.95},"input_digest":"ebcf54493c14dae2cde7dca0d7934a57bb97a050a7b4c39b5135cb67f626df5f","dataset_digests":{"thompson_how_partisan":"27dfd3e5b3af61d1d1cc217815059b9e48a979187dbb63e8b224be30ec1d24cc"},"result":{"tool":"infer.ols","version":"0.1.0","call_id":"call_3633dc63","status":"ok","reason":"","figures":[{"key":"coef.treat","value":-0.017313470048740457,"unit":"","label":"Coefficient on treat in the regression of share_detained_sheriff","se":0.012812938188523169,"ci_low":-0.04244117433763085,"ci_high":0.007814234240149934,"ci_level":0.95,"derivation":{"steps":[{"text_pt":"Coeficiente de treat na regressão por mínimos quadrados ordinários de share_detained_sheriff.","formula":"","terms":[{"symbol":"n","value":2056.0,"unit":"observações","note":""},{"symbol":"k","value":2.0,"unit":"parâmetros","note":""}],"result":-0.017313470048740457,"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":0.0001641713850229154,"unit":"","note":"diagonal da matriz de covariância"}],"result":0.012812938188523169,"unit":""},{"text_pt":"Intervalo de 95% de confiança: a estimativa mais ou menos o valor crítico da distribuição t de Student com 2054 graus de liberdade vezes o erro-padrão.","formula":"IC = β ± c · ep","terms":[{"symbol":"β","value":-0.017313470048740457,"unit":"","note":""},{"symbol":"c","value":1.961119605758953,"unit":"","note":"quantil 0.975"},{"symbol":"ep","value":0.012812938188523169,"unit":"","note":""}],"result":null,"unit":""}],"note_pt":""}}],"diagnostics":[{"name":"n_obs","value":2056,"flag":false,"note":"observations used"},{"name":"k","value":2,"flag":false,"note":"parameters estimated, fixed-effect dummies included"},{"name":"r2","value":0.0008902270941630874,"flag":false,"note":""},{"name":"df_resid","value":2054,"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":0.49502273212558573,"se":0.008743232146977581,"t":56.61781865150504,"p":0.0,"ci_low":0.47787620814444604,"ci_high":0.5121692561067254},{"term":"treat","coef":-0.017313470048740457,"se":0.012812938188523169,"t":-1.3512490104922628,"p":0.17676448914995885,"ci_low":-0.04244117433763085,"ci_high":0.007814234240149934}]},"datasets":{},"warnings":["1988 rows were dropped because one of 'share_detained_sheriff', 'treat' 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":"f7d3c9e118b9033fff62f95ff45f92f2123ea3335bc62608e8366c359a4dc625","params_versions":[],"sources":[],"started_at":"2026-10-15T17:11:43.821067Z","duration_ms":5.641458090394735,"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_9f63899b","llm_name":"infer__ols","vendor_id":"script_1","result_for_model_ref":"f8d806f9ac075b4384a398574f791546f017dece6727065d8ce0a71b1c181696"}}
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