{"call_id":"call_9f539f9d","run_id":"20261015T171059Z-suite-causcibench_real-scripted-ded01d/causcibench_real.q108/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":"2026-10-15T17:11:01.959752Z","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\": \"rebeira_does_rising\",\n \"rows\": 600,\n \"description\": \"This dataset aims to study the effect of income inequality on mortality rates for men and women in a subset of OECD countries from 1950 to 2000 using adult mortality rates at age sixty-five as the measure of mortality. Likewise, for measuring income inequality, the inverted Pareto-Lorenz coefficient was used. The coefficient is directly obtained from the World Top Income Database. More specifically, the measure was derived from individual taxation and income data as well as national account statistics. The Pareto-Lorenz coefficient ranges from 1.5 to 3. Values in the range of 1.5 to 1.8 are considered low inequality, while values above 2.5 are considered high inequality.\\n\\nData variables:\\nyear: Year observation was taken\\ncc: Country code\\nqxall: Averaged rate between qxfemale and qxmale\\nqxfemale: Five-year mortality rate for females at age sixty-five (the probability of dying within 5 years for 65-year-old women)\\ninvertedplc: Inverted Pareto-Lorenz coefficient (measure of income inequality at the top end of distribution, ranging from ~1.5-3)\\nexfemale: Life expectancy for females at age 65\\nqxmale: Five-year mortality rate for males at age sixty-five (the probability of dying within 5 years for 65-year-old men)\\nexmale: Life expectancy for males (at age 65)\\ncountry: Country name\\nrgdpe: Real GDP obtained from Penn World Table; values adjusted for purchasing power parity\\npop: Population (in millions)\\nhc: Health capital index - a measure capturing educational attainment that serves as a proxy for human capital stock\\nrpcgdp: Real per capita GDP - GDP per person adjusted for purchasing power parity\",\n \"columns\": {\n  \"year\": \"float64\",\n  \"cc\": \"str\",\n  \"qxfemale\": \"float64\",\n  \"invertedplc\": \"float64\",\n  \"exfemale\": \"float64\",\n  \"qxmale\": \"float64\",\n  \"exmale\": \"float64\",\n  \"country\": \"str\",\n  \"rgdpe\": \"float64\",\n  \"pop\": \"float64\",\n  \"hc\": \"float64\",\n  \"rpcgdp\": \"float64\",\n  \"qxall\": \"float64\"\n }\n}\n\n# Question\nDoes a greater inequality in income lead to a higher average mortality rate among males and females?","response":"causcibench_metadata_rule/1: ols\ninfer__ols({\"dataset\": \"rebeira_does_rising\", \"y\": \"qxall\", \"x\": [\"invertedplc\"], \"controls\": [\"pop\", \"hc\", \"rgdpe\"]})","tokens_in":0,"tokens_out":0,"meta":{"turn_call_id":"call_9f539f9d","text":"causcibench_metadata_rule/1: ols","tool_calls":[{"vendor_id":"script_1","llm_name":"infer__ols","tool_id":"infer.ols","args":{"dataset":"rebeira_does_rising","y":"qxall","x":["invertedplc"],"controls":["pop","hc","rgdpe"]}}],"request":{"system_stable_ref":"433a6877aa6a378bbd4b7e2752e484d8cb7499c562d68222be231f62ff2fa420","system_task_ref":"9fec18a8876f16d6605aac599828fbe811539828593d2e50d019885810f3c5b3","tools_ref":"52ab03d8ec104abb85572294bf8a469d75c5de9e74d6042caabff8017adc572b","contents_refs":[],"messages_refs":["ca0f75373f67f058a3f9a4a68f8bef25766f226b13c8af2dc82ffc583dbbbed0"],"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":1.0905000381171703}}
{"call_id":"call_bf96e082","run_id":"20261015T171059Z-suite-causcibench_real-scripted-ded01d/causcibench_real.q108/e1a1","step":"analysis","kind":"tool","name":"infer.ols","version":"0.1.0","code_version":"2925aeab2262","inputs":{"dataset":"rebeira_does_rising","y":"qxall","x":["invertedplc"],"controls":["pop","hc","rgdpe"],"fe":[],"cluster":null,"se":"HC1","weights":null,"subset":null,"intercept":true,"level":0.95},"input_digest":"1feefd6414b0f28e78fb29568b0b593a1c3b7778f8340cdcdd7aa25e400148dd","dataset_digests":{"rebeira_does_rising":"b8002d51d33a7f9a1278ba8db1cc069b65e05527316e73f35294457839af47ce"},"result":{"tool":"infer.ols","version":"0.1.0","call_id":"call_bf96e082","status":"ok","reason":"","figures":[{"key":"coef.invertedplc","value":-0.02486275668419051,"unit":"","label":"Coefficient on invertedplc in the regression of qxall","se":0.0036034497719140807,"ci_low":-0.031939784221834334,"ci_high":-0.017785729146546685,"ci_level":0.95,"derivation":{"steps":[{"text_pt":"Coeficiente de invertedplc na regressão por mínimos quadrados ordinários de qxall.","formula":"","terms":[{"symbol":"n","value":600.0,"unit":"observações","note":""},{"symbol":"k","value":5.0,"unit":"parâmetros","note":""}],"result":-0.02486275668419051,"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":0.00001298485025870764,"unit":"","note":"diagonal da matriz de covariância"}],"result":0.0036034497719140807,"unit":""},{"text_pt":"Intervalo de 95% de confiança: a estimativa mais ou menos o valor crítico da distribuição t de Student com 595 graus de liberdade vezes o erro-padrão.","formula":"IC = β ± c · ep","terms":[{"symbol":"β","value":-0.02486275668419051,"unit":"","note":""},{"symbol":"c","value":1.963958979754184,"unit":"","note":"quantil 0.975"},{"symbol":"ep","value":0.0036034497719140807,"unit":"","note":""}],"result":null,"unit":""}],"note_pt":""}}],"diagnostics":[{"name":"n_obs","value":600,"flag":false,"note":"observations used"},{"name":"k","value":5,"flag":false,"note":"parameters estimated, fixed-effect dummies included"},{"name":"r2","value":0.4811452120632217,"flag":false,"note":""},{"name":"df_resid","value":595,"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":0.2792920382842705,"se":0.007457768721117592,"t":37.449812233171116,"p":1.3052970655245668e-158,"ci_low":0.2646452864355017,"ci_high":0.2939387901330393},{"term":"invertedplc","coef":-0.02486275668419051,"se":0.0036034497719140807,"t":-6.899709516690144,"p":1.3372361435289914e-11,"ci_low":-0.031939784221834334,"ci_high":-0.017785729146546685},{"term":"pop","coef":0.00021859158094544688,"se":0.00003085453269160159,"t":7.0845856954089,"p":3.955960032361521e-12,"ci_low":0.0001579945443996569,"ci_high":0.00027918861749123684},{"term":"hc","coef":-0.045041054709449635,"se":0.0025800691052295715,"t":-17.45730554974493,"p":2.0636766592398935e-55,"ci_low":-0.05010820459705159,"ci_high":-0.03997390482184768},{"term":"rgdpe","coef":-4.164832973676447e-9,"se":1.0850870602708892e-9,"t":-3.8382477555641548,"p":0.0001371772012989165,"ci_low":-6.29589944951053e-9,"ci_high":-2.0337664978423645e-9}]},"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":"f41c6bca780caeba2a657c4da795d4381a807aa709b6042790f064f5c50b4113","params_versions":[],"sources":[],"started_at":"2026-10-15T17:11:01.963408Z","duration_ms":3.4949579276144505,"error":null,"decided_by":"model","rationale":"causcibench_metadata_rule/1: ols","cached":false,"informed_by":[],"model":null,"prompt":null,"response":null,"tokens_in":null,"tokens_out":null,"meta":{"turn_id":"call_9f539f9d","llm_name":"infer__ols","vendor_id":"script_1","result_for_model_ref":"7162ac5effd32e4aa25af3c1c06ad1160be07349b01578070d0d16551811f075"}}
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