{"call_id":"call_46a5315f","run_id":"20261015T171059Z-suite-causcibench_real-scripted-ded01d/causcibench_real.q145/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":"2026-10-15T17:11:04.018752Z","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\": \"broockman_do_female\",\n \"rows\": 5897,\n \"description\": \"The dataset aims to study the influence of electing women officials on women's participation in subsequent elections. It uses data from US districts with close election races between men and women candidates. In the contests, candidates with more than 50% of the vote win.\\n\\nVariable description:\\nYear: Election Year\\nState: US state abbreviation\\nChamber: Whether the election is for the House (H) or the Senate (S)\\nName: Name of the candidate contesting the current election\\nPty: Political party (R = republican, D = democrat, IP = independent) of the female candidate\\nSeat: Status of the contesting seat (I: incumbent, O = Open, etc.)\\nGE_Win: Outcome of the election (Won or lost)\\nwomanwon: Binary variable indicating whether a woman won the election or not (1 = Won, 0 = Lost)\\nfemalecand_margin_of_victory: Margin of victory for the candidates (negative values if the candidate lost)\\nfem_v_c_p_2: Margin of victory squared\\nfem_v_c_p_3: Margin of victory cubed\\nfem_v_c_p_4: Margin of victory raised to the fourth power\\nfemaleonballotnextyear: Share of women on the next election, i.e., women's voter turnout\\nwinXfem_v_c_p: Interaction between womanwon and femalecand_margin_of_victory\\nwinXfem_v_c_p_2: Interaction between womanwon and fem_v_c_p_2\\nwinXfem_v_c_p_3: Interaction between womanwon and fem_v_c_p_3\\nwinXfem_v_c_p_4: Interaction between womanwon and fem_v_c_p_4\\nabsolute_margin: absolute margin of victory of one candidate over the other in the last election, i.e., abs(femalecand_margin_of_victory)\\nfemaleonballotnextyear: Binary variable indicating if a female is on the ballot of the next year's elections (1 = yes, 0 = no)\\nfemaleturnout_thiscycle: Fraction of females that turned out to vote in the reference election\\nfemaleturnout_nextcycle: Fraction of females that turned out to vote in the next election\\nfemale_percentageofelectorate_th: Fraction of females in the voter electorate for the reference election\\nfemale_percentageofelectorate_ne: Fraction of females in the voter electorate for the next election\\noverall_voterturnout_thiscycle: Overall voter turnout in the reference election cycle\\noverall_voterturnout_nextcycle: Overall voter turnout in the next election cycle\",\n \"columns\": {\n  \"Year\": \"int64\",\n  \"State\": \"str\",\n  \"Chamber\": \"str\",\n  \"Name\": \"str\",\n  \"Pty\": \"str\",\n  \"Seat\": \"str\",\n  \"GE_Win\": \"str\",\n  \"womanwon\": \"int64\",\n  \"femalecand_margin_of_victory\": \"float64\",\n  \"fem_v_c_p_2\": \"float64\",\n  \"fem_v_c_p_3\": \"float64\",\n  \"fem_v_c_p_4\": \"float64\",\n  \"winXfem_v_c_p\": \"float64\",\n  \"winXfem_v_c_p_2\": \"float64\",\n  \"winXfem_v_c_p_3\": \"float64\",\n  \"winXfem_v_c_p_4\": \"float64\",\n  \"absolute_margin\": \"float64\",\n  \"next_cycle_year\": \"int64\",\n  \"year_plus_two\": \"int64\",\n  \"this_chamber_holds_elections_in_\": \"float64\",\n  \"VOTER_TURNOUT\": \"float64\",\n  \"femaleonballotnextyear\": \"float64\",\n  \"femaleturnout_thiscycle\": \"float64\",\n  \"femaleturnout_nextcycle\": \"float64\",\n  \"female_percentageofelectorate_th\": \"float64\",\n  \"female_percentageofelectorate_ne\": \"float64\",\n  \"overall_voterturnout_thiscycle\": \"float64\",\n  \"overall_voterturnout_nextcycle\": \"float64\",\n  \"GLASS_CEILINGS\": \"float64\",\n  \"femcands_over_contests_this_10r\": \"float64\",\n  \"femcands_over_contests_next_10r\": \"float64\",\n  \"femwins_over_contests_this_10r\": \"float64\",\n  \"femwins_over_contests_next_10r\": \"float64\",\n  \"femwins_over_femcands_this_10r\": \"float64\",\n  \"femwins_over_femcands_next_10r\": \"float64\",\n  \"femcands_thiscycle_10r\": \"int64\",\n  \"femcands_nextcycle_10r\": \"int64\",\n  \"numcontests_thiscycle_10r\": \"int64\",\n  \"numcontests_nextcycle_10r\": \"float64\",\n  \"femwins_thiscycle_10r\": \"float64\",\n  \"femwins_nextcycle_10r\": \"float64\",\n  \"femcands_over_contests_this_15r\": \"float64\",\n  \"femcands_over_contests_next_15r\": \"float64\",\n  \"femwins_over_contests_this_15r\": \"float64\",\n  \"femwins_over_contests_next_15r\": \"float64\",\n  \"femwins_over_femcands_this_15r\": \"float64\",\n  \"femwins_over_femcands_next_15r\": \"float64\",\n  \"femcands_thiscycle_15r\": \"int64\",\n  \"femcands_nextcycle_15r\": \"int64\",\n  \"numcontests_thiscycle_15r\": \"float64\",\n  \"numcontests_nextcycle_15r\": \"float64\",\n  \"femwins_thiscycle_15r\": \"float64\",\n  \"femwins_nextcycle_15r\": \"float64\",\n  \"femcands_over_contests_this_50m\": \"float64\",\n  \"femcands_over_contests_next_50m\": \"float64\",\n  \"femwins_over_contests_this_50m\": \"float64\",\n  \"femwins_over_contests_next_50m\": \"float64\",\n  \"femwins_over_femcands_this_50m\": \"float64\",\n  \"femwins_over_femcands_next_50m\": \"float64\",\n  \"femcands_thiscycle_50m\": \"int64\",\n  \"femcands_nextcycle_50m\": \"int64\",\n  \"numcontests_thiscycle_50m\": \"float64\",\n  \"numcontests_nextcycle_50m\": \"float64\",\n  \"femwins_thiscycle_50m\": \"float64\",\n  \"femwins_nextcycle_50m\": \"float64\",\n  \"femcands_over_contests_this_75m\": \"float64\",\n  \"femcands_over_contests_next_75m\": \"float64\",\n  \"femwins_over_contests_this_75m\": \"float64\",\n  \"femwins_over_contests_next_75m\": \"float64\",\n  \"femwins_over_femcands_this_75m\": \"float64\",\n  \"femwins_over_femcands_next_75m\": \"float64\",\n  \"femcands_thiscycle_75m\": \"int64\",\n  \"femcands_nextcycle_75m\": \"int64\",\n  \"numcontests_thiscycle_75m\": \"float64\",\n  \"numcontests_nextcycle_75m\": \"float64\",\n  \"femwins_thiscycle_75m\": \"float64\",\n  \"femwins_nextcycle_75m\": \"float64\",\n  \"femcands_over_contests_this_100m\": \"float64\",\n  \"femcands_over_contests_next_100m\": \"float64\",\n  \"femwins_over_contests_this_100m\": \"float64\",\n  \"femwins_over_contests_next_100m\": \"float64\",\n  \"femwins_over_femcands_this_100m\": \"float64\",\n  \"femwins_over_femcands_next_100m\": \"float64\",\n  \"femcands_thiscycle_100m\": \"int64\",\n  \"femcands_nextcycle_100m\": \"int64\",\n  \"numcontests_thiscycle_100m\": \"float64\",\n  \"numcontests_nextcycle_100m\": \"float64\",\n  \"femwins_thiscycle_100m\": \"float64\",\n  \"femwins_nextcycle_100m\": \"float64\",\n  \"femcands_over_contests_this_150m\": \"float64\",\n  \"femcands_over_contests_next_150m\": \"float64\",\n  \"femwins_over_contests_this_150m\": \"float64\",\n  \"femwins_over_contests_next_150m\": \"float64\",\n  \"femwins_over_femcands_this_150m\": \"float64\",\n  \"femwins_over_femcands_next_150m\": \"float64\",\n  \"femcands_thiscycle_150m\": \"int64\",\n  \"femcands_nextcycle_150m\": \"int64\",\n  \"numcontests_thiscycle_150m\": \"float64\",\n  \"numcontests_nextcycle_150m\": \"float64\",\n  \"femwins_thiscycle_150m\": \"float64\",\n  \"femwins_nextcycle_150m\": \"float64\",\n  \"femcands_over_contests_this100ms\": \"float64\",\n  \"femcands_over_contests_next100ms\": \"float64\",\n  \"femwins_over_contests_this100ms\": \"float64\",\n  \"femwins_over_contests_next100ms\": \"float64\",\n  \"femwins_over_femcands_this100ms\": \"float64\",\n  \"femwins_over_femcands_next100ms\": \"float64\",\n  \"femcands_thiscycle100ms\": \"float64\",\n  \"femcands_nextcycle100ms\": \"float64\",\n  \"numcontests_thiscycle100ms\": \"float64\",\n  \"numcontests_nextcycle100ms\": \"float64\",\n  \"femwins_thiscycle100ms\": \"float64\",\n  \"femwins_nextcycle100ms\": \"float64\",\n  \"femcands_over_contests_this_50ms\": \"float64\",\n  \"femcands_over_contests_next_50ms\": \"float64\",\n  \"femwins_over_contests_this_50ms\": \"float64\",\n  \"femwins_over_contests_next_50ms\": \"float64\",\n  \"femwins_over_femcands_this_50ms\": \"float64\",\n  \"femwins_over_femcands_next_50ms\": \"float64\",\n  \"femcands_thiscycle_50ms\": \"float64\",\n  \"femcands_nextcycle_50ms\": \"float64\",\n  \"numcontests_thiscycle_50ms\": \"float64\",\n  \"numcontests_nextcycle_50ms\": \"float64\",\n  \"femwins_thiscycle_50ms\": \"float64\",\n  \"femwins_nextcycle_50ms\": \"float64\",\n  \"femcands_over_contests_this5r\": \"float64\",\n  \"femcands_over_contests_next5r\": \"float64\",\n  \"femwins_over_contests_this5r\": \"float64\",\n  \"femwins_over_contests_next5r\": \"float64\",\n  \"femwins_over_femcands_this5r\": \"float64\",\n  \"femwins_over_femcands_next5r\": \"float64\"\n }\n}\n\n# Question\nDoes electing women in districts with close races empower other women to vote at the next election?","response":"causcibench_metadata_rule/1: rdd\ninfer__ols({\"dataset\": \"broockman_do_female\", \"y\": \"femaleonballotnextyear\", \"x\": [\"womanwon\"], \"controls\": [\"femalecand_margin_of_victory\", \"fem_v_c_p_2\", \"fem_v_c_p_3\", \"fem_v_c_p_4\", \"winXfem_v_c_p\", \"winXfem_v_c_p_2\", \"winXfem_v_c_p_3\", \"winXfem_v_c_p_4\"]})","tokens_in":0,"tokens_out":0,"meta":{"turn_call_id":"call_46a5315f","text":"causcibench_metadata_rule/1: rdd","tool_calls":[{"vendor_id":"script_1","llm_name":"infer__ols","tool_id":"infer.ols","args":{"dataset":"broockman_do_female","y":"femaleonballotnextyear","x":["womanwon"],"controls":["femalecand_margin_of_victory","fem_v_c_p_2","fem_v_c_p_3","fem_v_c_p_4","winXfem_v_c_p","winXfem_v_c_p_2","winXfem_v_c_p_3","winXfem_v_c_p_4"]}}],"request":{"system_stable_ref":"433a6877aa6a378bbd4b7e2752e484d8cb7499c562d68222be231f62ff2fa420","system_task_ref":"693ffc3a799611ae249204597b725f742b85a8d216977efa8b4ed42728768b43","tools_ref":"52ab03d8ec104abb85572294bf8a469d75c5de9e74d6042caabff8017adc572b","contents_refs":[],"messages_refs":["d30a0a39f03923c54196069689b5fae832aa5b27a7a12ef3869396d424edb9e2"],"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":1.5265829861164093}}
{"call_id":"call_53a47bbf","run_id":"20261015T171059Z-suite-causcibench_real-scripted-ded01d/causcibench_real.q145/e1a1","step":"analysis","kind":"tool","name":"infer.ols","version":"0.1.0","code_version":"2925aeab2262","inputs":{"dataset":"broockman_do_female","y":"femaleonballotnextyear","x":["womanwon"],"controls":["femalecand_margin_of_victory","fem_v_c_p_2","fem_v_c_p_3","fem_v_c_p_4","winXfem_v_c_p","winXfem_v_c_p_2","winXfem_v_c_p_3","winXfem_v_c_p_4"],"fe":[],"cluster":null,"se":"HC1","weights":null,"subset":null,"intercept":true,"level":0.95},"input_digest":"6eb18b38847d72408a562ad1d5c3aa4a3815b0e5605db20fd70b7b1eba20a76b","dataset_digests":{"broockman_do_female":"950d0458bf6c4207813a05b15376261911c645df01034cf394a37843c48c7f1c"},"result":{"tool":"infer.ols","version":"0.1.0","call_id":"call_53a47bbf","status":"ok","reason":"","figures":[{"key":"coef.womanwon","value":0.4001986575718106,"unit":"","label":"Coefficient on womanwon in the regression of femaleonballotnextyear","se":0.044837606575893973,"ci_low":0.3122997335536824,"ci_high":0.4880975815899388,"ci_level":0.95,"derivation":{"steps":[{"text_pt":"Coeficiente de womanwon na regressão por mínimos quadrados ordinários de femaleonballotnextyear.","formula":"","terms":[{"symbol":"n","value":5660.0,"unit":"observações","note":""},{"symbol":"k","value":10.0,"unit":"parâmetros","note":""}],"result":0.4001986575718106,"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":0.0020104109634546505,"unit":"","note":"diagonal da matriz de covariância"}],"result":0.044837606575893973,"unit":""},{"text_pt":"Intervalo de 95% de confiança: a estimativa mais ou menos o valor crítico da distribuição t de Student com 5650 graus de liberdade vezes o erro-padrão.","formula":"IC = β ± c · ep","terms":[{"symbol":"β","value":0.4001986575718106,"unit":"","note":""},{"symbol":"c","value":1.9603839439856556,"unit":"","note":"quantil 0.975"},{"symbol":"ep","value":0.044837606575893973,"unit":"","note":""}],"result":null,"unit":""}],"note_pt":""}}],"diagnostics":[{"name":"n_obs","value":5660,"flag":false,"note":"observations used"},{"name":"k","value":10,"flag":false,"note":"parameters estimated, fixed-effect dummies included"},{"name":"r2","value":0.28569732107550905,"flag":false,"note":""},{"name":"df_resid","value":5650,"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":0.48426935135067484,"se":0.037191431532915674,"t":13.020992507967328,"p":3.282945956306249e-38,"ci_low":0.4113598661197051,"ci_high":0.5571788365816446},{"term":"womanwon","coef":0.4001986575718106,"se":0.044837606575893973,"t":8.925513383378703,"p":5.900292846184959e-19,"ci_low":0.3122997335536824,"ci_high":0.4880975815899388},{"term":"femalecand_margin_of_victory","coef":1.8543824466485932,"se":1.2293603776547264,"t":1.5084124072602967,"p":0.13150492239962058,"ci_low":-0.5556358990778743,"ci_high":4.264400792375061},{"term":"fem_v_c_p_2","coef":3.184085977868888,"se":11.743974546862383,"t":0.2711250748341902,"p":0.7863047412222585,"ci_low":-19.83861316237634,"ci_high":26.20678511811412},{"term":"fem_v_c_p_3","coef":-8.206691500622835,"se":40.16731889283446,"t":-0.20431265334184018,"p":0.8381165414964167,"ci_low":-86.9500585310872,"ci_high":70.53667552984152},{"term":"fem_v_c_p_4","coef":-17.529608573606282,"se":43.89942359557533,"t":-0.3993129553385095,"p":0.6896777148794544,"ci_low":-103.5893337405972,"ci_high":68.53011659338463},{"term":"winXfem_v_c_p","coef":-2.2001913509095985,"se":1.4756816304168785,"t":-1.4909661444305211,"p":0.13602620857790568,"ci_low":-5.093093925613422,"ci_high":0.6927112237942246},{"term":"winXfem_v_c_p_2","coef":-4.004250405356269,"se":13.922064837251455,"t":-0.28761900279634117,"p":0.7736489457148079,"ci_low":-31.29684277943129,"ci_high":23.28834196871875},{"term":"winXfem_v_c_p_3","coef":20.372959525465077,"se":46.797920860623115,"t":0.435338988373891,"p":0.6633330178857106,"ci_low":-71.36893314161185,"ci_high":112.114852192542},{"term":"winXfem_v_c_p_4","coef":-1.0365138993160725,"se":50.22502691774267,"t":-0.02063739858245671,"p":0.983535635792117,"ci_low":-99.49685025510617,"ci_high":97.42382245647401}]},"datasets":{},"warnings":["237 rows were dropped because one of 'femaleonballotnextyear', 'womanwon', 'femalecand_margin_of_victory', 'fem_v_c_p_2', 'fem_v_c_p_3', 'fem_v_c_p_4', 'winXfem_v_c_p', 'winXfem_v_c_p_2', 'winXfem_v_c_p_3', 'winXfem_v_c_p_4' was missing"],"assumptions":["the conditional mean of the outcome is linear in the regressors","errors are independent across observations; 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