{"call_id":"call_c1199dd3","run_id":"20261015T170025Z-suite-qrdata_socsci-baseline-2a40bf/qrdata_socsci.367/e1a1","step":"analysis","kind":"llm","name":"llm.analyst.baseline","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":"2026-10-15T17:00:25.887557Z","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\": \"learning_mindset\",\n \"rows\": 10391,\n \"description\": \"The National Study of Learning Mindsets is a randomised study conducted in U.S. public high schools which aims at finding the impact of a growth mindset. The way it works is that students receive from the school a seminar to instill in them a growth mindset. Then, they follow up the students in their college years to measure how well they’ve performed academically. This measurement was compiled into an achievement score and standardized. The CSV file learning_mindset.csv contains simulated data of this research.\\nVariable Description\\nintervention: the intervention of the growth mindset;\\nachievement_score: the standardized academic achievement score;\\nschoolid: identifier of the student’s school;\\nsuccess_expect: self-reported expectations for success in the future, a proxy for prior achievement, measured prior to random assignment;\\nethnicity: categorical variable for student race/ethnicity;\\ngender: categorical variable for student identified gender;\\nfrst_in_family: categorical variable for student first-generation status, i.e. first in family to go to college;\\nschool_urbanicity: school-level categorical variable for urbanicity of the school, i.e. rural, suburban, etc;\\nschool_mindset: school-level mean of students’ fixed mindsets, reported prior to random assignment, standardized;\\nschool_achievement: school achievement level, as measured by test scores and college preparation for the previous 4 cohorts of students, standardized;\\nschool_ethnic_minority: school racial/ethnic minority composition, i.e., percentage of student body that is Black, Latino, or Native American, standardized;\\nschool_poverty: school poverty concentration, i.e., percentage of students who are from families whose incomes fall below the federal poverty line, standardized;\\nschool_size: total number of students in all four grade levels in the school, standardized.\",\n \"columns\": {\n  \"schoolid\": \"int64\",\n  \"intervention\": \"int64\",\n  \"achievement_score\": \"float64\",\n  \"success_expect\": \"int64\",\n  \"ethnicity\": \"int64\",\n  \"gender\": \"int64\",\n  \"frst_in_family\": \"int64\",\n  \"school_urbanicity\": \"int64\",\n  \"school_mindset\": \"float64\",\n  \"school_achievement\": \"float64\",\n  \"school_ethnic_minority\": \"float64\",\n  \"school_poverty\": \"float64\",\n  \"school_size\": \"float64\"\n }\n}\n\n# Question\nWhat is the average treatment effect of the growth mindset on the achievement score? Please conduct doubly robust estimation, use logistic regression to estimate the propensity score, and round the final answer to the nearest hundredth.","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_c1199dd3","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":"7412fdda27526a7c90f8832d4e6d6f77b880c6bb52d45d838b2f3abfb7ccf621","tools_ref":"52ab03d8ec104abb85572294bf8a469d75c5de9e74d6042caabff8017adc572b","contents_refs":[],"messages_refs":["ba5cdcb929623e6a018b1dfc075d7cdbeba5fb897293006387d3de0f80d4bbe9"],"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":0.4208749160170555}}
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