{"call_id":"call_ff6d740c","run_id":"20261015T171059Z-suite-causcibench_real-scripted-ded01d/causcibench_real.q074/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":"2026-10-15T17:11:00.944143Z","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\": \"panagopoulos_timing\",\n \"rows\": 25000,\n \"description\": \"The dataset consists of 25,000 registered voters in Rochester, New York, who were part of a randomized field experiment during the November 2005 municipal elections. Voters were randomly assigned to one of four groups: a phone reminder call scheduled for 4 weeks before Election Day, 2 weeks before, 3 days before, or a control group that received no call. The calls were nonpartisan get-out-the-vote reminders, delivered using standardized scripts. The unit of observation is the individual registered voter, and only those appearing on the official registration list were included. The main outcome is voter turnout in the 2005 election, while additional measures capture past voting history, party registration, demographics, and a predicted propensity to vote. The dataset also records whether each assigned reminder call was actually completed, allowing for comparison between group assignment and the reality of who was contacted.\\n\\nData variables:\\nvtr_gen01: Voted (2001)\\nvtr_gen03: Voted (2003)\\nvtr_gen05: Outcome: Voted 2005\\ntreated: Successfully contacted\\nfour_weeks: Experimental group: Called 4 weeks before election\\ntwo_weeks: Experimental group: Called 2 weeks before election\\nthree_days: Experimental group: Called 3 days before election\\ncontrol: Experimental group: Control, i.e., did not receive any calls\\ndemocrat: Registered Democrat\\nrepublican: Registered Republican\\nunenrolled: Unaffiliated\\nother_party: Other party registration\\nage: Age (years)\\nage_squared: Age squared\\ncontacted_4weeks: Successfully contacted: 4 weeks\\ncontacted_2weeks: Successfully contacted: 2 weeks\\ncontacted_3days: Successfully contacted: 3 days\\nvote_prop: Predicted vote propensity\\nuse: Subject included\\nid: Subject ID\",\n \"columns\": {\n  \"vtr_gen01\": \"int64\",\n  \"vtr_gen03\": \"int64\",\n  \"vtr_gen05\": \"int64\",\n  \"treated\": \"int64\",\n  \"four_weeks\": \"int64\",\n  \"two_weeks\": \"int64\",\n  \"three_days\": \"int64\",\n  \"control\": \"int64\",\n  \"democrat\": \"int64\",\n  \"republican\": \"int64\",\n  \"unenrolled\": \"int64\",\n  \"other_party\": \"int64\",\n  \"age\": \"float64\",\n  \"age_squared\": \"float64\",\n  \"contacted_4weeks\": \"int64\",\n  \"contacted_2weeks\": \"int64\",\n  \"contacted_3days\": \"int64\",\n  \"vote_prop\": \"float64\",\n  \"use\": \"int64\",\n  \"id\": \"int64\"\n }\n}\n\n# Question\nWhat is the effect of receiving a get-out-the-vote phone call two weeks prior to the election on voter turnout?","response":"causcibench_metadata_rule/1: iv\ninfer__iv_2sls({\"dataset\": \"panagopoulos_timing\", \"y\": \"vtr_gen05\", \"endog\": [\"contacted_2weeks\"], \"instruments\": [\"two_weeks\"], \"exog\": [\"democrat\", \"republican\", \"unenrolled\", \"other_party\", \"age\", \"age_squared\", \"vtr_gen01\", \"vtr_gen03\"]})","tokens_in":0,"tokens_out":0,"meta":{"turn_call_id":"call_ff6d740c","text":"causcibench_metadata_rule/1: iv","tool_calls":[{"vendor_id":"script_1","llm_name":"infer__iv_2sls","tool_id":"infer.iv_2sls","args":{"dataset":"panagopoulos_timing","y":"vtr_gen05","endog":["contacted_2weeks"],"instruments":["two_weeks"],"exog":["democrat","republican","unenrolled","other_party","age","age_squared","vtr_gen01","vtr_gen03"]}}],"request":{"system_stable_ref":"433a6877aa6a378bbd4b7e2752e484d8cb7499c562d68222be231f62ff2fa420","system_task_ref":"7b3fcdc38051078d800f8ac2314d8764e0862cd7fed2975749ab5f608bcf0ee0","tools_ref":"52ab03d8ec104abb85572294bf8a469d75c5de9e74d6042caabff8017adc572b","contents_refs":[],"messages_refs":["e724c57ee23d1486197141e9b9626a66a72111f8073c6bdea2cd40f347413e5c"],"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":0.7789582014083862}}
{"call_id":"call_15a2e27a","run_id":"20261015T171059Z-suite-causcibench_real-scripted-ded01d/causcibench_real.q074/e1a1","step":"analysis","kind":"tool","name":"infer.iv_2sls","version":"0.1.0","code_version":"2925aeab2262","inputs":{"dataset":"panagopoulos_timing","y":"vtr_gen05","endog":["contacted_2weeks"],"instruments":["two_weeks"],"exog":["democrat","republican","unenrolled","other_party","age","age_squared","vtr_gen01","vtr_gen03"],"cluster":null,"se":"HC1","subset":null,"level":0.95},"input_digest":"4132a7ba3dc92bbdbacdb1dfe11c64936bab0d6f04bab2923131325cd5c3f8e4","dataset_digests":{"panagopoulos_timing":"a9840be060a94f8bcf5d9657e12a817f7e5a23d132aea3f8161124cbc615d87e"},"result":{"tool":"infer.iv_2sls","version":"0.1.0","call_id":"call_15a2e27a","status":"ok","reason":"","figures":[{"key":"coef.contacted_2weeks","value":-0.006193464656590043,"unit":"","label":"2SLS effect of contacted_2weeks on vtr_gen05","se":0.013344682267538401,"ci_low":-0.0323498716111985,"ci_high":0.01996294229801841,"ci_level":0.95,"derivation":{"steps":[{"text_pt":"Com um instrumento e um regressor endógeno, o MQ2E é a razão entre o coeficiente de two_weeks na forma reduzida (vtr_gen05 sobre two_weeks e os controles) e no primeiro estágio (contacted_2weeks sobre two_weeks e os controles).","formula":"β = ρ / π","terms":[{"symbol":"ρ","value":-0.004558184874756851,"unit":"","note":"forma reduzida"},{"symbol":"π","value":0.7359668824309513,"unit":"","note":"primeiro estágio"}],"result":-0.006193464656590043,"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":0.00017808054482155384,"unit":"","note":"diagonal da matriz de covariância"}],"result":0.013344682267538401,"unit":""},{"text_pt":"Intervalo de 95% de confiança: a estimativa mais ou menos o valor crítico da distribuição t de Student com 24161 graus de liberdade vezes o erro-padrão.","formula":"IC = β ± c · ep","terms":[{"symbol":"β","value":-0.006193464656590043,"unit":"","note":""},{"symbol":"c","value":1.960062175345696,"unit":"","note":"quantil 0.975"},{"symbol":"ep","value":0.013344682267538401,"unit":"","note":""}],"result":null,"unit":""}],"note_pt":""}}],"diagnostics":[{"name":"first_stage_F.contacted_2weeks","value":5410.969556892448,"flag":false,"note":"Wald F of the excluded instruments in the first stage for contacted_2weeks (heteroskedasticity-robust standard errors (HC1))"},{"name":"partial_r2.contacted_2weeks","value":0.7197658679525261,"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":24171,"flag":false,"note":""},{"name":"se_type","value":"HC1","flag":false,"note":"heteroskedasticity-robust standard errors 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clustered"}],"tables":{"coefficients":[{"term":"Intercept","coef":-0.006257336627638613,"se":0.017594404524171166,"t":-0.35564355810066167,"p":0.7221106237269574,"ci_low":-0.0407434634331977,"ci_high":0.028228790177920477},{"term":"democrat","coef":0.0183861942851418,"se":0.015504519076553534,"t":1.185860341385631,"p":0.235689119057504,"ci_low":-0.01200362710373656,"ci_high":0.048776015674020165},{"term":"republican","coef":-0.019437526783793003,"se":0.015714181440513786,"t":-1.2369417304600931,"p":0.21612073852452518,"ci_low":-0.050238299441863414,"ci_high":0.011363245874277408},{"term":"unenrolled","coef":-0.031278588626889266,"se":0.015814997124021603,"t":-1.9777802285768245,"p":0.0479648492100454,"ci_low":-0.06227696629288497,"ci_high":-0.00028021096089355865},{"term":"other_party","coef":-0.009640503825993385,"se":0.018269507729042508,"t":-0.5276827361181798,"p":0.5977244032058135,"ci_low":-0.04544987488787545,"ci_high":0.026168867235888675},{"term":"age","coef":0.004363944843953094,"se":0.0003564883801715629,"t":12.241478507245903,"p":2.3591311461029977e-34,"ci_low":0.003665205454028557,"ci_high":0.005062684233877631},{"term":"age_squared","coef":-0.000020756070170859407,"se":2.1578835359915916e-6,"t":-9.618716591820869,"p":7.29531174899702e-22,"ci_low":-0.00002498565606855775,"ci_high":-0.000016526484273161066},{"term":"vtr_gen01","coef":0.29046760316663633,"se":0.007504282764356268,"t":38.70691074519408,"p":0.0,"ci_low":0.275758742367123,"ci_high":0.30517646396614967},{"term":"vtr_gen03","coef":0.3398614042038497,"se":0.007421877251512969,"t":45.791838464394985,"p":0.0,"ci_low":0.3253140633331004,"ci_high":0.35440874507459896},{"term":"contacted_2weeks","coef":-0.006193464656590043,"se":0.013344682267538401,"t":-0.46411480861226284,"p":0.6425696740961453,"ci_low":-0.0323498716111985,"ci_high":0.01996294229801841}],"first_stage":[{"endog":"contacted_2weeks","instrument":"two_weeks","coef":0.7359668824309513,"se":0.010005083714648276,"t":73.5592927976639,"p":0.0,"ci_low":0.7163562962807021,"ci_high":0.7555774685812006}],"reduced_form":[{"instrument":"two_weeks","coef":-0.004558184874756851,"se":0.009819861089868947,"t":-0.4641801786238591,"p":0.6425228432782131,"ci_low":-0.023805723164157935,"ci_high":0.014689353414644234}]},"datasets":{},"warnings":["829 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