{"call_id":"call_083e7627","run_id":"20261015T170037Z-suite-qrdata_socsci-naive-761cb5/qrdata_socsci.391/e1a1","step":"analysis","kind":"llm","name":"llm.analyst.scripted","version":"","code_version":"62cdbe0860ad","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":"2026-10-15T17:00:38.335387Z","duration_ms":0.0,"error":null,"decided_by":"model","rationale":"","cached":false,"informed_by":[],"model":"naive-ols","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\": \"face\",\n \"rows\": 119,\n \"description\": \"Several psychologists have reported the intriguing result of an experiment showing that facial appearance predicts election outcomes better than chance. In their experiment, the researchers briefly showed student subjects the black-and-white head shots of two candidates from a US congressional election (winner and runner-up). The exposure of subjects to facial pictures lasted less than a second, and the subjects were then asked to evaluate the two candidates in terms of their perceived competence.\\nThe researchers used these competence measures to predict election outcomes. The key hypothesis is whether or not a within-a-second evaluation of facial appearance can predict election outcomes. The CSV data set, face.csv, contains the data from the experiment. Note that we include data only from subjects who did not know the candidates’ political parties, their policies, or even which candidate was the incumbent or challenger. They were simply making snap judgments about which candidate appeared more competent based on their facial expression alone.\\n\\nVariable Description\\ncongress: session of Congress\\nyear: year of the election\\nstate: state of the election\\nwinner: name of the winner\\nloser: name of the runner-up\\nw.party: party of the winner\\nl.party: party of the loser\\nd.votes: number of votes for the Democratic candidate\\nr.votes: number of votes for the Republican candidate\\nd.comp: competence measure for the Democratic candidate\\nr.comp: competence measure for the Republican candidate\",\n \"columns\": {\n  \"year\": \"int64\",\n  \"state\": \"str\",\n  \"winner\": \"str\",\n  \"loser\": \"str\",\n  \"w.party\": \"str\",\n  \"l.party\": \"str\",\n  \"d.comp\": \"float64\",\n  \"r.comp\": \"float64\",\n  \"d.votes\": \"int64\",\n  \"r.votes\": \"int64\"\n }\n}\n\n# Question\nFit a linear regression model using the Democratic margin in the two-party vote share as the response variable and the perceived competence for Democratic candidates as the predictor. What is the estimated slope of the model? Please round to the nearest thousandth.","response":"submit_answer({\"answer\": \"Linha de base ingênua: o item não declara o resultado e o tratamento, então não há regressão ingênua a fazer e nenhuma estimativa é citada.\", \"figure_refs\": [], \"caveats\": []})","tokens_in":0,"tokens_out":0,"meta":{"turn_call_id":"call_083e7627","text":"","tool_calls":[{"vendor_id":"script_1","llm_name":"submit_answer","tool_id":null,"args":{"answer":"Linha de base ingênua: o item não declara o resultado e o tratamento, então não há regressão ingênua a fazer e nenhuma estimativa é citada.","figure_refs":[],"caveats":[]}}],"request":{"system_stable_ref":"433a6877aa6a378bbd4b7e2752e484d8cb7499c562d68222be231f62ff2fa420","system_task_ref":"16ddc29a6197662de513f06b6d935de4d7b02b07d511ff47d24cc029dd641e4f","tools_ref":"52ab03d8ec104abb85572294bf8a469d75c5de9e74d6042caabff8017adc572b","contents_refs":[],"messages_refs":["393e07da80495e327d1f3b3c857050aac6782e11bec323bbeeabca30d2573dbc"],"config":{}},"attempts":[],"response_id":null,"finish_reason":"tool_use","model_reported":"naive-ols","usage":{"input":0,"output":0,"thoughts":0,"cached":0},"thoughts":null,"vendor":false,"wall_ms":0.2402078825980425}}
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