{"call_id":"call_0c177567","run_id":"20261015T171142Z-suite-causcibench_real-naive-d6494d/causcibench_real.q118/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":"2026-10-15T17:11:44.968917Z","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\": \"garcia_colored_perceptions\",\n \"rows\": 5600,\n \"description\": \"The present study is based on an original survey experiment that tests whether assessments of others’ skin color are affected by a subtle racial cue, a name. Random assignment achieved a fairly uniform distribution of subjects across conditions, with 2,783 unique ratings of faces assigned Hispanic names and 2,817 unique ratings of faces assigned non-Hispanic names. Because each subject rated pictures with both Hispanic and non-Hispanic names, subject characteristics were necessarily balanced across experimental conditions.\\n\\nVariables: \\n- id: Respondent’s unique ID number\\n- imageno: Unique image number, from the FEI face database\\n- hispanicimage: Was the image presented with a distinctively Hispanic name or a distinctively non-Hispanic name? (1 = Distinctively Hispanic name, NOT INCLUDED IN DATASET)\\n- femaleimage: Does the person in the image present as male or female? (1 = Female, 0 = Male)\\n- hispanicimagexfemaleimage: Interaction between hispanicimage and femaleimage\\n- namepair: To which pair of names was the image attached? (\\\"\\\"a\\\"\\\" = Catalina/Catherine, \\\"\\\"b\\\"\\\" = Juana/Joann, \\\"\\\"c\\\"\\\" = Margarita/Margaret. \\\"\\\"d\\\"\\\" = Carlota/Charlotte, \\\"\\\"e\\\"\\\" = Daniela/Daniella, \\\"\\\"f\\\"\\\" = Juan/John, \\\"\\\"g\\\"\\\" = Pedro/Peter, \\\"\\\"h\\\"\\\" = Ricardo/Richard, \\\"\\\"i\\\"\\\" = Pablo/Paul, \\\"\\\"j\\\"\\\" = Jose/Joe)\\n- color: Skin color rating of image (1 = Lightest; 11 = Darkest)\\n- gender: Respondent's gender (1 = Male; 2 = Female)\\n- age: Respondent's age\\n- hispanic: Respondent's identification as Hispanic/Latino(a) (1 = Yes; 2 = No)\\n- race: Respondent's racial identification (1 = White, 2 = Black/African-American; 3 = American Indian or Alaska Native; 4 = Asian or Pacific Islander; 5 = Some other race)\\n- educ: Respondent's educational attainment (1 = Less than high school; 2 = High school/GED; 3 = Some college; 4 = 2-year college degree (Associate’s); 5 = 4-year college degree (BA, BS); 6 = Graduate or professional degree)\\n- hhinc: Respondent's household inome from all sources (1 = Less than $10,000; 2 = $10,000- $19,999; 3 = $20,000-$29,999; 4 = $30,000-$39,999; 5 = $40,000-$49,999; 6 = $50,000-$59,999; 7 = $60,000- $69,999; 8 = $70,000-$79,999; 9 = $80,000-$89,999; 10 = $90,000-$99,999; 11 = $100,000-$149,999; 12 = More than $150,000; 13 = Rather not say)\\n- born: Respondent's country of birth\\n- region: Respondent's US Census region of residence (1 = Northeast; 2 = Midwest; 3 = South; 4 = West; 5 = Rather not say)\\n- rs_color: Respondent's self-rated skin color (1 = Lightest; 11 = Darkest)\",\n \"columns\": {\n  \"id\": \"int64\",\n  \"imageno\": \"int64\",\n  \"hispanicimagexfemaleimage\": \"int64\",\n  \"femaleimage\": \"int64\",\n  \"namepair\": \"str\",\n  \"color\": \"int64\",\n  \"gender\": \"int64\",\n  \"age\": \"int64\",\n  \"hispanic\": \"int64\",\n  \"race\": \"int64\",\n  \"educ\": \"int64\",\n  \"hhinc\": \"int64\",\n  \"born\": \"int64\",\n  \"region\": \"int64\",\n  \"rs_color\": \"int64\",\n  \"hispanicname\": \"int64\"\n }\n}\n\n# Question\nDo people consider that the skin color of hispanic people is darker when the person presented is a woman?","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_0c177567","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":"491e3c71e85428933da0e5fa9fdf7387a6a7907462ea3128e94173674191f7c9","tools_ref":"52ab03d8ec104abb85572294bf8a469d75c5de9e74d6042caabff8017adc572b","contents_refs":[],"messages_refs":["a04138733cba3f8e75f0568834122e31c3727d451d2d8d61e42613f0c015bcf5"],"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":0.19670813344419003}}
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