{"call_id":"call_a0098c79","run_id":"20261015T171142Z-suite-causcibench_real-naive-d6494d/causcibench_real.q168/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":"2026-10-15T17:11:47.209912Z","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\": \"brogaard_interpreting_performance2\",\n \"rows\": 3018,\n \"description\": \"\\\"This study examines public attitudes and cognitive responses regarding organizational performance across different service sectors throuh randomized survey experiments. The research utilizes data from large-scale nationally representative surveys conducted to understand how individuals process and evaluate information about service delivery organizations. The dataset captures responses from participants who were presented with various scenarios involving performance metrics for different types of service providers across multiple public service domains.\\n\\nVariable Descriptions:\\nRecordNo: Unique identifier for each survey respondent\\nweight: Statistical weighting factor for survey representativeness\\nQ1: Primary evaluation question response\\nQ2_1: Secondary evaluation question response (first component)\\nQ2_2: Secondary evaluation question response (second component)\\nQ2_3: Secondary evaluation question response (third component)\\nQ_P: Performance assessment question for baseline scenario\\nQ_T1 through Q_T6: Performance assessment questions for six different scenario variations\\nScenario: Type of service scenario presented to respondent\\nRandom_Placebo: Random assignment indicator for control condition\\nRandom_T1_T6: Random assignment indicators for various scenario conditions\\nScenario_new: Recoded scenario type variable\\ntreatment: Consolidated experimental condition assignment\\ngender: Respondent's gender identification\\nprofile_age: Age of the respondent\\nregion: Geographic region of residence\\nurban: Urban versus rural residence indicator\\nprofile_education: Educational attainment level\\nOld_Private_better: Previous assessment of private sector performance superiority\\nOld_More_contracting_out: Previous support for increased outsourcing of public services\\nOld_Public_service_preference: Previous preference for public service delivery\\nP_correct: Accuracy of interpretation for baseline performance scenario\\nT1_correct through T6_correct: Accuracy of interpretation for each of the six scenario variations\\nMale_dummy: Binary indicator for male respondents\\nage_35_49: Binary indicator for respondents aged 35-49 years\\nage_50_64: Binary indicator for respondents aged 50-64 years\\nage_18_34: Binary indicator for respondents aged 18-34 years\\nPrivate_better: Recoded measure of private sector performance assessment\\nMore_contracting_out: Recoded measure of outsourcing support\\nPublic_service_preference: Recoded measure of public service delivery preference\\nQ_P_new: Alternative coding of baseline performance question\\nPlacebo: Alternative coding of control condition response\\nT1_P through T6_P: Alternative coding of performance questions for six scenarios\\nDepT1_P through DepT6_P: Dependent variable formulations for six experimental conditions\\\"\",\n \"columns\": {\n  \"RecordNo\": \"float64\",\n  \"Q1\": \"str\",\n  \"Q2_1\": \"str\",\n  \"Q2_2\": \"str\",\n  \"Q2_3\": \"str\",\n  \"Q_P\": \"str\",\n  \"Q_T1\": \"str\",\n  \"Q_T2\": \"str\",\n  \"Q_T3\": \"str\",\n  \"Q_T4\": \"str\",\n  \"Q_T5\": \"str\",\n  \"Q_T6\": \"str\",\n  \"Scenario\": \"str\",\n  \"Random_Placebo\": \"str\",\n  \"Random_T1_T6\": \"str\",\n  \"gender\": \"str\",\n  \"profile_age\": \"str\",\n  \"region\": \"str\",\n  \"urban\": \"str\",\n  \"profile_education\": \"str\",\n  \"weight\": \"float64\",\n  \"Old_Public_service_preference\": \"float64\",\n  \"T1_correct\": \"str\",\n  \"T2_correct\": \"str\",\n  \"P_correct\": \"str\",\n  \"T3_correct\": \"str\",\n  \"T4_correct\": \"str\",\n  \"T5_correct\": \"str\",\n  \"T6_correct\": \"str\",\n  \"Male_dummy\": \"str\",\n  \"age_35_49\": \"str\",\n  \"age_50_64\": \"str\",\n  \"age_18_34\": \"str\",\n  \"Private_better\": \"str\",\n  \"More_contracting_out\": \"str\",\n  \"Public_service_preference\": \"float64\",\n  \"T1_P\": \"str\",\n  \"T2_P\": \"str\",\n  \"T3_P\": \"str\",\n  \"T4_P\": \"str\",\n  \"T5_P\": \"str\",\n  \"T6_P\": \"str\",\n  \"DepT2_P\": \"str\",\n  \"DepT3_P\": \"str\",\n  \"DepT4_P\": \"str\",\n  \"DepT5_P\": \"str\",\n  \"DepT6_P\": \"str\",\n  \"Pub_service_pref_T1_P\": \"float64\",\n  \"Pub_service_pref_T2_P\": \"float64\",\n  \"Placebo\": \"str\",\n  \"DepT1_P\": \"str\",\n  \"treatment\": \"float64\",\n  \"Scenario_new\": \"str\"\n }\n}\n\n# Question\nWhat is the effect on perceived refuse collection quality due to provider-type information assuming better public vs. worse for-profit among people with public service preference between 0 and 25?","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_a0098c79","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 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