{"call_id":"call_3df41bc6","run_id":"20261015T171059Z-suite-causcibench_real-scripted-ded01d/causcibench_real.q154/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":"2026-10-15T17:11:04.070940Z","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\": \"zoorob_privatization\",\n \"rows\": 6276,\n \"description\": \"This dataset aims to study the impact of privatizing jail medical services on healthcare quality using statistics associated with U.S. jails from 2008-2019. The dataset is structured at the jail-year level and links treatment timing and vendor status to a per-inmate-day healthcare measure, with an indicator for coverage in an external Reuters dataset.\\n\\nVariables include:\\n\\nJail: Unique identifier for the jail or jail system, stable across years\\nRelative_Treated: Event-time variable indicating years relative to treatment onset for treated jails; used to construct leads and lags in event-study specifications and typically undefined for never-treated units\\nyear: Calendar year of observation from 2008 to 2019\\nfirst.treated: The first year in which the jail contracts with a private medical provider; for never-treated units this is missing or set to a non-applicable value\\nIn_Reuters_Data: Indicator equal to 1 if the jail can be linked to the Reuters investigative dataset on jail healthcare providers, and 0 otherwise\\nContract_Medical: Indicator equal to 1 if medical services are provided by a private contractor in that jail-year, and 0 if services are publicly provided\\nMed_per_inmateday: Primary outcome measuring healthcare quantity or quality per inmate-day; this is a rate normalized by the inmate population and exposure time (e.g., clinic visits, sick-call responses, adverse events, or expenditures per inmate-day, depending on the specific operational definition)\\\"\",\n \"columns\": {\n  \"id\": \"int64\",\n  \"year\": \"int64\",\n  \"health_provider\": \"str\",\n  \"average_daily_population\": \"float64\",\n  \"total_deaths_annual\": \"float64\",\n  \"fips\": \"float64\",\n  \"statecode\": \"str\",\n  \"state\": \"str\",\n  \"state_notes\": \"str\",\n  \"county\": \"str\",\n  \"jail\": \"str\",\n  \"_FACID\": \"str\",\n  \"death_rate\": \"float64\",\n  \"illness_suicide_death_rate\": \"float64\",\n  \"private_provider\": \"float64\",\n  \"public_provider\": \"float64\",\n  \"large_provider\": \"float64\",\n  \"log_death_rate\": \"float64\",\n  \"asinh_death_rate\": \"float64\",\n  \"first.treat\": \"int64\",\n  \"last.treat\": \"int64\",\n  \"first.treat.public\": \"int64\",\n  \"last.treat.public\": \"int64\",\n  \"first.treat.large\": \"int64\",\n  \"last.treat.large\": \"int64\",\n  \"TreatmentStatus\": \"str\",\n  \"UnitExitsTreatment\": \"int64\",\n  \"UnitExitsTreatmentPublic\": \"int64\",\n  \"AlwaysMissingOrAlwaysTreated\": \"int64\",\n  \"AlwaysTreatedWhenDVNotMissing\": \"int64\"\n }\n}\n\n# Question\nWhat is the effect of privatizing jail healthcare on overall inmate death rates?","response":"causcibench_metadata_rule/1: did\ninfer__ols({\"dataset\": \"zoorob_privatization\", \"y\": \"death_rate\", \"x\": [\"private_provider\"], \"controls\": [], \"fe\": [\"id\", \"year\"]})","tokens_in":0,"tokens_out":0,"meta":{"turn_call_id":"call_3df41bc6","text":"causcibench_metadata_rule/1: did","tool_calls":[{"vendor_id":"script_1","llm_name":"infer__ols","tool_id":"infer.ols","args":{"dataset":"zoorob_privatization","y":"death_rate","x":["private_provider"],"controls":[],"fe":["id","year"]}}],"request":{"system_stable_ref":"433a6877aa6a378bbd4b7e2752e484d8cb7499c562d68222be231f62ff2fa420","system_task_ref":"53014b1d17f3174735a4998e0715fa434e0226203b2d8c1ac5ddbe2a83d28550","tools_ref":"52ab03d8ec104abb85572294bf8a469d75c5de9e74d6042caabff8017adc572b","contents_refs":[],"messages_refs":["f5e740aab0b18f1e62320abee3c44176b2c1976333cb1bfbdf755e25134c1c9e"],"config":{}},"attempts":[],"response_id":null,"finish_reason":"tool_use","model_reported":"reference","usage":{"input":0,"output":0,"thoughts":0,"cached":0},"thoughts":null,"vendor":false,"wall_ms":0.5903749261051416}}
{"call_id":"call_b492eacf","run_id":"20261015T171059Z-suite-causcibench_real-scripted-ded01d/causcibench_real.q154/e1a1","step":"analysis","kind":"tool","name":"infer.ols","version":"0.1.0","code_version":"2925aeab2262","inputs":{"dataset":"zoorob_privatization","y":"death_rate","x":["private_provider"],"controls":[],"fe":["id","year"],"cluster":null,"se":"HC1","weights":null,"subset":null,"intercept":true,"level":0.95},"input_digest":"f8c57e5cdb6fe5989f139e1026692f59d434c38dd5d82837d0b6db775fd1eb8e","dataset_digests":{"zoorob_privatization":"0065769e4b4340c2bc24760a93bd5fd33607d84cb55454e277ca7a01d4b3deb4"},"result":{"tool":"infer.ols","version":"0.1.0","call_id":"call_b492eacf","status":"ok","reason":"","figures":[{"key":"coef.private_provider","value":-0.11507903103883849,"unit":"","label":"Coefficient on private_provider in the regression of death_rate","se":0.11723683146191245,"ci_low":-0.34491004025352956,"ci_high":0.11475197817585261,"ci_level":0.95,"derivation":{"steps":[{"text_pt":"Coeficiente de private_provider na regressão por mínimos quadrados ordinários de death_rate, efeitos fixos de id, year.","formula":"","terms":[{"symbol":"n","value":5982.0,"unit":"observações","note":""},{"symbol":"k","value":532.0,"unit":"parâmetros","note":""}],"result":-0.11507903103883849,"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":0.013744474651228866,"unit":"","note":"diagonal da matriz de covariância"}],"result":0.11723683146191245,"unit":""},{"text_pt":"Intervalo de 95% de confiança: a estimativa mais ou menos o valor crítico da distribuição t de Student com 5450 graus de liberdade vezes o erro-padrão.","formula":"IC = β ± c · ep","terms":[{"symbol":"β","value":-0.11507903103883849,"unit":"","note":""},{"symbol":"c","value":1.9603993587062944,"unit":"","note":"quantil 0.975"},{"symbol":"ep","value":0.11723683146191245,"unit":"","note":""}],"result":null,"unit":""}],"note_pt":""}}],"diagnostics":[{"name":"n_obs","value":5982,"flag":false,"note":"observations used"},{"name":"k","value":532,"flag":false,"note":"parameters estimated, fixed-effect dummies included"},{"name":"r2","value":0.20183177689934906,"flag":false,"note":""},{"name":"df_resid","value":5450,"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":"id (520 levels), year (12 levels)","flag":false,"note":"absorbed as dummies; their coefficients are not tabulated"}],"tables":{"coefficients":[{"term":"Intercept","coef":0.46175229234285065,"se":0.4015197645394994,"t":1.1500113646271723,"p":0.2501896499271201,"ci_low":-0.3253867965682863,"ci_high":1.2488913812539875},{"term":"private_provider","coef":-0.11507903103883849,"se":0.11723683146191245,"t":-0.9815945177282023,"p":0.3263432212286315,"ci_low":-0.34491004025352956,"ci_high":0.11475197817585261}]},"datasets":{},"warnings":["294 rows were dropped because one of 'death_rate', 'private_provider', 'id', '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":"7d4829dde8531ca582b909c749fe6918a56ba26956477830958bb12d91c187c9","params_versions":[],"sources":[],"started_at":"2026-10-15T17:11:04.073519Z","duration_ms":183.57045901939273,"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_3df41bc6","llm_name":"infer__ols","vendor_id":"script_1","result_for_model_ref":"7fb5c19843658a9fbc6cfff9b06abafb7f0cdb00983e89bc091119b71fca7bb7"}}
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