{"call_id":"call_1aea76a6","run_id":"20261015T171059Z-suite-causcibench_real-scripted-ded01d/causcibench_real.q151/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":"2026-10-15T17:11:03.930587Z","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\": \"cirone_cabinets\",\n \"rows\": 8147,\n \"description\": \"The dataset comes from French parliamentary records across multiple legislative sessions. It is structured by deputy-year, and includes variables for legislative behavior, career success, policy-making, tenure, and constituent characteristics. An interesting feature of the lower house was that the legislature was randomly assigned into 11 groups called bureaux, and each group chose members to serve on the budget committee. The deputies convened with their assigned group, and after hours of discussion, each bureau nominated three representatives to serve on the budget committee. Bureaux was a temporary committee, and the selection was done every year. This random selection of bureaux (or bureau) for budget selection was done from 1894 to 1910, after which it was disbanded.\\n\\nVariable description:\\nid: ID number (Unique identifier for each deputy)\\nyear: Year (1894-1909)\\nterm: Term (1893, 1898, 1902, or 1906)\\nleader: Deputy was any type of party leader (defined as a member of a parliamentary governing committee) (1 = yes, 0 = no)\\ncentrist_all: Deputy was a member of a Radical or Radical-Socialist party (1 = Radical socialist, 0 = radical party)\\ninscrits: Number of electors in deputy's district (in 000s)\\npermargin: Electoral margin of prior election, as a ratio of total votes cast\\nparis: Whether the deputy represents a Parisian district or suburb (1 = Parisian district, 0 = suburb)\\nage: Age of deputy\\nbureau: Deputy's bureau number (assignment) for that year\\nbudget: Deputy was on the budget committee (1 = yes, 0 = no)\\nmin: Deputy was a minister (1 = yes, 0 = no)\\ntermyear: Identifier for term + year\\nbudgettermyears: Number of years a deputy has served on the budget committee, including the current year\\nbudgetexptermyears: Budget experience in years\\nproprietaire: Deputy was a property owner (upper class) (1 = yes, 0 = no)\\nlib_all: Deputy held a liberal profession (middle class) (1 = yes, 0 = no)\\ncivil: Deputy was a civil servant (1 = yes, 0 = no)\\nreelect: Deputy was reelected the next term (1 = yes, 0 = no)\\nF1to5firstminyear: Became a minister for the first time within 5 years (1 = yes, 0 = no)\\nF1to5firstleaderyear: Became a party leader within 5 years (1 = yes, 0 = no)\\ncummyears: Years of parliamentary experience\\nclburyear: Identifier for term year + bureau\\nbudgetincumbent: Deputy was a budget incumbent (1 = yes, 0 = no)\\nbureauotherbudgetincumbent: Number of budget incumbents in deputy's bureau (excluding deputy)\\nreelectterm: Deputy was reelected the next term (1 = yes, 0 = no)\\nreelecttwoterm: Deputy was reelected within the next two terms (1 = yes, 0 = no)\\nsenate: Deputy was a senator (1 = yes, 0 = no)\\nrannext: Deputy ran for reelection in the subsequent term (1 = yes, 0 = no)\\nF1to5senateyear: Became a senator within 5 years (1 = yes, 0 = no)\\nF1to5billdummy: Sponsored a bill within 5 years (1 = yes, 0 = no)\\nF1to5billfinancedummy: Sponsored a finance bill within 5 years (1 = yes, 0 = no)\\nF1to5billecondummy: Sponsored an econ bill within 5 years (1 = yes, 0 = no)\\nF1to5billwelfaredummy: Sponsored a welfare bill within 5 years (1 = yes, 0 = no)\\nF1to5billbudgetdummy: Sponsored a budget bill within 5 years (1 = yes, 0 = no)\\nF1to5billagendadummy: Sponsored an agenda bill within 5 years (1 = yes, 0 = no)\\nF1to5billinterpdummy: Sponsored an interpellation bill within 5 years (1 = yes, 0 = no)\\nF1to5cosponsordummy: Cosponsored a budget bill within 5 years (1 = yes, 0 = no)\\nF1to5cosponsordummy_anybm: Co-sponsored with current or previous budget committee member within 5 years (1 = yes, 0 = no)\\ncummyears2: Years of parliamentary experience squared\\nage2: Age, squared\\ninscrits2: Electors, squared\\npermargin2: Electoral Margin of Prior Election, squared\\ndeptcode: Department ID\",\n \"columns\": {\n  \"id\": \"float64\",\n  \"year\": \"float64\",\n  \"term\": \"float64\",\n  \"leader\": \"float64\",\n  \"centrist_all\": \"float64\",\n  \"inscrits\": \"float64\",\n  \"permargin\": \"float64\",\n  \"paris\": \"float64\",\n  \"age\": \"float64\",\n  \"bureau\": \"int64\",\n  \"budget\": \"float64\",\n  \"min\": \"float64\",\n  \"termyear\": \"float64\",\n  \"budgettermyears\": \"float64\",\n  \"budgetexptermyears\": \"float64\",\n  \"proprietaire\": \"float64\",\n  \"lib_all\": \"float64\",\n  \"civil\": \"float64\",\n  \"reelect\": \"float64\",\n  \"F1to5firstminyear\": \"float64\",\n  \"F1to5firstleaderyear\": \"float64\",\n  \"cummyears\": \"float64\",\n  \"clburyear\": \"float64\",\n  \"budgetincumbent\": \"float64\",\n  \"bureauotherbudgetincumbent\": \"float64\",\n  \"reelectterm\": \"float64\",\n  \"reelecttwoterm\": \"float64\",\n  \"senate\": \"float64\",\n  \"rannext\": \"float64\",\n  \"F1to5senateyear\": \"float64\",\n  \"F1to5billdummy\": \"float64\",\n  \"F1to5billfinancedummy\": \"float64\",\n  \"F1to5billecondummy\": \"float64\",\n  \"F1to5billwelfaredummy\": \"float64\",\n  \"F1to5billbudgetdummy\": \"float64\",\n  \"F1to5billagendadummy\": \"float64\",\n  \"F1to5billinterpdummy\": \"float64\",\n  \"F1to5cosponsordummy\": \"float64\",\n  \"F1to5cosponsordummy_anybm\": \"float64\",\n  \"cummyears2\": \"float64\",\n  \"age2\": \"float64\",\n  \"inscrits2\": \"float64\",\n  \"permargin2\": \"float64\",\n  \"deptcode\": \"float64\"\n }\n}\n\n# Question\nWhat is the effect of being appointed to the budget committee on the probability a deputy sponsors a budget amendment within the next five years?","response":"causcibench_metadata_rule/1: iv\ninfer__iv_2sls({\"dataset\": \"cirone_cabinets\", \"y\": \"F1to5billbudgetdummy\", \"endog\": [\"budget\"], 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{"call_id":"call_e715aefd","run_id":"20261015T171059Z-suite-causcibench_real-scripted-ded01d/causcibench_real.q151/e1a1","step":"analysis","kind":"tool","name":"infer.iv_2sls","version":"0.1.0","code_version":"2925aeab2262","inputs":{"dataset":"cirone_cabinets","y":"F1to5billbudgetdummy","endog":["budget"],"instruments":["bureauotherbudgetincumbent"],"exog":["age","age2","inscrits","inscrits2","permargin","permargin2","cummyears","cummyears2","proprietaire","lib_all","civil","paris","budgetincumbent"],"cluster":null,"se":"HC1","subset":null,"level":0.95},"input_digest":"21db5140d7713a7a1fe1e8c59317019cf594928c1fd82de822b4d1c444897f1f","dataset_digests":{"cirone_cabinets":"07239c56e4ad82cd189262eaf36350b339b83df4a4eea3c51ba6ba5423663916"},"result":{"tool":"infer.iv_2sls","version":"0.1.0","call_id":"call_e715aefd","status":"ok","reason":"","figures":[{"key":"coef.budget","value":0.7646518738560152,"unit":"","label":"2SLS effect of budget on F1to5billbudgetdummy","se":0.27407663443046193,"ci_low":0.22739157589204928,"ci_high":1.301912171819981,"ci_level":0.95,"derivation":{"steps":[{"text_pt":"Com um instrumento e um regressor endógeno, o MQ2E é a razão entre o coeficiente de bureauotherbudgetincumbent na forma reduzida (F1to5billbudgetdummy sobre bureauotherbudgetincumbent e os controles) e no primeiro estágio (budget sobre bureauotherbudgetincumbent e os controles).","formula":"β = ρ / π","terms":[{"symbol":"ρ","value":-0.006371794566214163,"unit":"","note":"forma reduzida"},{"symbol":"π","value":-0.008332935266452998,"unit":"","note":"primeiro estágio"}],"result":0.7646518738560152,"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":0.07511800154072908,"unit":"","note":"diagonal da matriz de covariância"}],"result":0.27407663443046193,"unit":""},{"text_pt":"Intervalo de 95% de confiança: a estimativa mais ou menos o valor crítico da distribuição t de Student com 8132 graus de liberdade vezes o erro-padrão.","formula":"IC = β ± c · ep","terms":[{"symbol":"β","value":0.7646518738560152,"unit":"","note":""},{"symbol":"c","value":1.9602557477415257,"unit":"","note":"quantil 0.975"},{"symbol":"ep","value":0.27407663443046193,"unit":"","note":""}],"result":null,"unit":""}],"note_pt":""}}],"diagnostics":[{"name":"first_stage_F.budget","value":35.90535017542439,"flag":false,"note":"Wald F of the excluded instruments in the first stage for budget (heteroskedasticity-robust standard errors (HC1))"},{"name":"partial_r2.budget","value":0.00409673990936732,"flag":false,"note":"share of the variation the instruments explain beyond the controls"},{"name":"weak_instrument","value":false,"flag":false,"note":"every first-stage F is at least 10"},{"name":"n_obs","value":8147,"flag":false,"note":""},{"name":"se_type","value":"HC1","flag":false,"note":"heteroskedasticity-robust standard 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