{"call_id":"call_aab165fc","run_id":"20261015T171122Z-suite-causcibench_real-baseline-4f971b/causcibench_real.q048/e1a1","step":"analysis","kind":"llm","name":"llm.analyst.baseline","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":"2026-10-15T17:11:23.598466Z","duration_ms":0.0,"error":null,"decided_by":"model","rationale":"","cached":false,"informed_by":[],"model":"rules-1","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_congressional\",\n \"rows\": 12618,\n \"description\": \"The dataset contains congressional election results from 1950-2006. Time 1 refers to midterm election years, and Time 2 denotes years when presidential elections took place. Besides the election results, the dataset also contains data on election spending, incumbent political party, indicators for whether the seat is open or not, etc.\\n\\nThe variables are:\\ndv_c_t1: Democratic Congressional Vote Share Time 1 (margin of victory)\\ndv_c_t2: Democratic Congressional Vote Share Time 2 (margin of victory)\\ndv_p_t2: Democratic Presidential Vote Share Time 2 (margin of victory)\\nt2_year: Year of the Time 2 election\\nt1_atlargeormulticandidate: Indicator equal to 1 if the state has multiple at-large Congressional seats at T1\\nt1_openseat: Indicator equal to 1 if the T1 House race was an open seat\\nt2_3rdpartyinc: Indicator equal to 1 if a third-party incumbent\\nt2_demfrinc: Indicator equal to 1 if the T2 House incumbent is a Democratic freshman\\nt2_incumbent_has_switched_prty: Indicator equal to 1 if the House incumbent switched party between T1 and T2\\nt2_is_midterm: Indicator equal to 1 if T2 is a midterm congressional election year rather than a presidential year\\nt2_opend: Indicator equal to 1 if the T2 House race is an open seat previously held by a Democrat\\nt2_openr: Indicator equal to 1 if the T2 House race is an open seat previously held by a Republican\\nt2_openseat: Indicator equal to 1 if the T2 House race is an open seat\\nt2_redist: Indicator equal to 1 if redistricting occurred between T1 and T2 for the district\\nt2_repfrinc: Indicator equal to 1 if the T2 House incumbent is a Republican freshman\\nt2_specialelectiontoeelect: Indicator equal to 1 if a special election between T1 and T2 was held\\nt2_missingdexp: Indicator equal to 1 if Democratic House candidate expenditure at T2 is missing\\nt2_missingrexp: Indicator equal to 1 if Republican House candidate expenditure at T2 is missing\\nt2_dexp: Democratic House candidate expenditure at T2\\nt2_rexp: Republican House candidate expenditure at T2\\nt2_icf: Inflation Conversion Factors\\nsouth: Indicator equal to 1 if the district is in a Southern state\",\n \"columns\": {\n  \"dv_c_t1\": \"float64\",\n  \"dv_c_t2\": \"float64\",\n  \"dv_p_t2\": \"float64\",\n  \"t2_year\": \"int64\",\n  \"t1_atlargeormulticandidate\": \"float64\",\n  \"t1_openseat\": \"int64\",\n  \"t2_3rdpartyinc\": \"float64\",\n  \"t2_demfrinc\": \"int64\",\n  \"t2_incumbent_has_switched_prty\": \"float64\",\n  \"t2_is_midterm\": \"int64\",\n  \"t2_opend\": \"float64\",\n  \"t2_openr\": \"float64\",\n  \"t2_openseat\": \"float64\",\n  \"t2_redist\": \"float64\",\n  \"t2_repfrinc\": \"int64\",\n  \"t2_specialelectiontoeelect\": \"float64\",\n  \"t2_missingdexp\": \"int64\",\n  \"t2_missingrexp\": \"int64\",\n  \"t2_dexp\": \"float64\",\n  \"t2_rexp\": \"float64\",\n  \"t2_icf\": \"float64\",\n  \"south\": \"float64\"\n }\n}\n\n# Question\nWhat is the effect of having a congressional incumbent on the party's presidential vote share in that district at the next election?","response":"submit_answer({\"answer\": \"Pelas regras deste roteador não identifiquei um desenho que responda à pergunta; nenhuma estimativa foi feita.\", \"figure_refs\": [], \"caveats\": []})","tokens_in":0,"tokens_out":0,"meta":{"turn_call_id":"call_aab165fc","text":"","tool_calls":[{"vendor_id":"baseline_answer","llm_name":"submit_answer","tool_id":null,"args":{"answer":"Pelas regras deste roteador não identifiquei um desenho que responda à pergunta; nenhuma estimativa foi feita.","figure_refs":[],"caveats":[]}}],"request":{"system_stable_ref":"433a6877aa6a378bbd4b7e2752e484d8cb7499c562d68222be231f62ff2fa420","system_task_ref":"0d1c4796abe965cf3e01fe2f1a40ac2686e3e69a94e0e0f0bf8912bb0ca1d995","tools_ref":"52ab03d8ec104abb85572294bf8a469d75c5de9e74d6042caabff8017adc572b","contents_refs":[],"messages_refs":["353b569eda917fe573fc0a28a475deaebed740be5caf3786e347ac9d824e1c47"],"config":{}},"attempts":[],"response_id":null,"finish_reason":"tool_use","model_reported":"rules-1","usage":{"input":0,"output":0,"thoughts":0,"cached":0},"thoughts":null,"vendor":false,"wall_ms":0.9618340991437435}}
{"call_id":"call_eb0caa18","run_id":"20261015T171122Z-suite-causcibench_real-baseline-4f971b/causcibench_real.q048/e1a1","step":"analysis","kind":"note","name":"analyst.guard","version":"","code_version":"2925aeab2262","inputs":{},"input_digest":"","dataset_digests":{},"result":null,"outputs":null,"output_digest":"","params_versions":[],"sources":[],"started_at":"2026-10-15T17:11:23.601562Z","duration_ms":0.0,"error":null,"decided_by":"code","rationale":"","cached":false,"informed_by":[],"model":null,"prompt":null,"response":null,"tokens_in":null,"tokens_out":null,"meta":{"turn_id":"call_aab165fc","numbers_found":[],"unbacked":[],"uncited":[],"invalid_refs":[],"empty":false,"verdict":"accepted","retries_left":1,"vendor_id":"baseline_answer"}}