{"call_id":"call_ca5b2d1e","run_id":"20261015T170957Z-suite-crb_papers-baseline-dae0c1/crb_papers.q094/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":"2026-10-15T17:09:59.845032Z","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\": \"ds_0062\",\n \"rows\": 132003,\n \"description\": \"| Variable Name | Description                                                                                                       |\\n|---------------|-------------------------------------------------------------------------------------------------------------------|\\n| voted         | Binary indicator (1/0) that the parent cast a ballot in the 2013 Danish municipal election, validated from official polling-place records. |\\n| days          | Child’s age in days relative to the legal voting-age threshold on the election day (0 = eligibility birthday falls on election day; negative = days before reaching eligibility; positive = days after). Values may appear at half-day increments due to preprocessing. |\\n\\nData Description: \\nThis file is a de-identified extract of Danish administrative data centered on parents whose child was near the legal voting-age threshold at the time of the 2013 municipal election. Each row corresponds to one parent linked to one child whose birthday placed them within a specified window around the eligibility age on election day, recording whether the parent voted and the child’s relative age in days.\\n\\nThe dataset was constructed by linking national population registers (which include exact dates of birth and parent–child links via the Civil Registration System) with validated turnout from official polling-place voter lists compiled by municipalities for the 2013 election. For 2013, the turnout linkage covered virtually the entire electorate nationwide. The resulting file contains de-identified records that provide a clean snapshot of parent voting behavior and precise child age timing relative to the legal voting-age threshold.\",\n \"columns\": {\n  \"voted\": \"int64\",\n  \"days\": \"float64\"\n }\n}\n\n# Question\nAmong Danish parents whose child's age is near the voting eligibility threshold in 2013, what is the average causal effect of the child becoming eligible to vote on the parent's probability of voting in that 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_ca5b2d1e","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":"de7e41d8c6590fa4542eae3b0da6cdf4b9a733d79c1ba7f155db7d32443ca1f7","tools_ref":"52ab03d8ec104abb85572294bf8a469d75c5de9e74d6042caabff8017adc572b","contents_refs":[],"messages_refs":["a9ce1e184e8c4de77780ef490b56ac4ff0cda8ec1146381d205308afd7230506"],"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":0.31974981538951397}}
{"call_id":"call_cc3cbd26","run_id":"20261015T170957Z-suite-crb_papers-baseline-dae0c1/crb_papers.q094/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":"2026-10-15T17:09:59.847182Z","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_ca5b2d1e","numbers_found":[],"unbacked":[],"uncited":[],"invalid_refs":[],"empty":false,"verdict":"accepted","retries_left":1,"vendor_id":"baseline_answer"}}