{"call_id":"call_9ad065e0","run_id":"20261015T171142Z-suite-causcibench_real-naive-d6494d/causcibench_real.q126/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":"2026-10-15T17:11:45.173930Z","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\": \"pereira_does_electing\",\n \"rows\": 8116,\n \"description\": \"The study aims to investigate whether there is a causal link between the share of women in office and malfeasance in Spain. In 2007, Spain's Organic Law 3/2007 on Effective Equality between Women and Men introduced mandatory gender quotas for electoral lists. The law established a 40% minimum quota requirement, mandating that political parties include at least 40% (and no more than 60%) of candidates of each sex on every electoral list.\\n\\nVariable description:\\nmuni_code: statistical code for each municipality\\nWelec: Number of women elected in 2007\\nMainParty: Most voted party 2007\\nAlcParty: Mayor's party\\nFemAlc: Woman mayor = 1\\npop: Population\\nWshare: Share of women in council\\nWshare03: Share of women in council 2003-2007\\nFemAlc03: Woman mayor in 2003 = 1\\nArea_hec: Municipality area\\nopland07: Open land 2007\\npop03: Population 2003\\ncorr0711: Corruption cases 07-11\\ncorr0307: Corruption cases 03-07\\ncorr1115: Corruption cases 11-15\\nnuc711: Change in urban land 07-11\\nnuc37: Change in urban land 03-07\\nnvac7: Vacant land\\nPP03: PP mayor 2003\\nPSOE03: PSOE mayor 2003\\nec: Economic activity index 2006\\nconst: Construction sector index 2006\\ntur: Tourism sector index 2006\\nind: Industry sector index 2006\\ncom: Business index 2006\\nbancos: Number of banks\",\n \"columns\": {\n  \"muni_code\": \"int64\",\n  \"Welec\": \"int64\",\n  \"MainParty\": \"str\",\n  \"AlcParty\": \"str\",\n  \"FemAlc\": \"int64\",\n  \"pop\": \"int64\",\n  \"Wshare\": \"float64\",\n  \"Wshare03\": \"float64\",\n  \"FemAlc03\": \"float64\",\n  \"Area_hec\": \"float64\",\n  \"opland07\": \"float64\",\n  \"pop03\": \"int64\",\n  \"corr0711\": \"int64\",\n  \"corr1115\": \"int64\",\n  \"nuc1115\": \"float64\",\n  \"nuc711\": \"float64\",\n  \"nuc37\": \"float64\",\n  \"nvac7\": \"float64\",\n  \"PP03\": \"float64\",\n  \"PSOE03\": \"float64\",\n  \"ec\": \"float64\",\n  \"const\": \"float64\",\n  \"tur\": \"float64\",\n  \"ind\": \"float64\",\n  \"com\": \"float64\",\n  \"bancos\": \"float64\",\n  \"corr0307\": \"int64\"\n }\n}\n\n# Question\nWhat is the effect of the gender quota enacted in 2007 on corruption?","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_9ad065e0","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 citada.","figure_refs":[],"caveats":[]}}],"request":{"system_stable_ref":"433a6877aa6a378bbd4b7e2752e484d8cb7499c562d68222be231f62ff2fa420","system_task_ref":"f8e96d77de2c52ba464b5d520378d81816144a0e4bc1fbc9b45dabb7417b2b17","tools_ref":"52ab03d8ec104abb85572294bf8a469d75c5de9e74d6042caabff8017adc572b","contents_refs":[],"messages_refs":["a4c15fbc70dfd9d6ee6334a50aa5b39a8e6657d11c53b980e12d0a9030b731ff"],"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":0.22887508384883404}}
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