{"call_id":"call_66606758","run_id":"20261015T171401Z-suite-tool_selection-baseline-a1426d/tool_selection.panel_simpson.test.s1001/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":"2026-10-15T17:14:02.492382Z","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\": \"dados\",\n \"rows\": 640,\n \"description\": \"Uma linha por município e ano. municipio: código do município; ano: ano da eleição; doacoes: doações de campanha recebidas pelo partido no município (R$ por eleitor); votos: parcela dos votos obtida pelo partido no município.\",\n \"columns\": {\n  \"municipio\": \"int64\",\n  \"ano\": \"int64\",\n  \"doacoes\": \"float64\",\n  \"votos\": \"float64\"\n }\n}\n\n# Question\nOs municípios que recebem mais doações de campanha dão menos votos ao partido? Temos 8 anos de dados por município e queremos saber o que acontece com os votos quando as doações aumentam dentro de um mesmo município ao longo do tempo.","response":"As mesmas unidades ao longo do tempo: efeitos fixos de unidade.\ninfer__fe({\"dataset\": \"dados\", \"y\": \"votos\", \"x\": [\"doacoes\"], \"unit\": \"municipio\"})","tokens_in":0,"tokens_out":0,"meta":{"turn_call_id":"call_66606758","text":"As mesmas unidades ao longo do tempo: efeitos fixos de unidade.","tool_calls":[{"vendor_id":"baseline_1","llm_name":"infer__fe","tool_id":"infer.fe","args":{"dataset":"dados","y":"votos","x":["doacoes"],"unit":"municipio"}}],"request":{"system_stable_ref":"433a6877aa6a378bbd4b7e2752e484d8cb7499c562d68222be231f62ff2fa420","system_task_ref":"ad61990735565242d8f9c50ebe1db99e888f8236042ee2260507325aa181366d","tools_ref":"52ab03d8ec104abb85572294bf8a469d75c5de9e74d6042caabff8017adc572b","contents_refs":[],"messages_refs":["4419213200f0646f5bafe38ffabb02c77df56af191fd501957b50aa60d04e00b"],"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":0.736375106498599}}
{"call_id":"call_59fcb57e","run_id":"20261015T171401Z-suite-tool_selection-baseline-a1426d/tool_selection.panel_simpson.test.s1001/e1a1","step":"analysis","kind":"tool","name":"infer.fe","version":"0.1.0","code_version":"2925aeab2262","inputs":{"dataset":"dados","y":"votos","x":["doacoes"],"unit":"municipio","time":null,"cluster":null,"level":0.95},"input_digest":"60b79c9f8d537bd25d11fdb9093fcc4f791eab58afc7e21a250a1c350524d5a6","dataset_digests":{"dados":"062b9c01a01877ea6553a3c95b8ff95ba19ba2260814383df3d2a478645fec3d"},"result":{"tool":"infer.fe","version":"0.1.0","call_id":"call_59fcb57e","status":"ok","reason":"","figures":[{"key":"coef.doacoes","value":0.014083451691745961,"unit":"","label":"Within-unit effect of doacoes on votos","se":0.00014920293506096418,"ci_low":0.013786470678286913,"ci_high":0.01438043270520501,"ci_level":0.95,"derivation":{"steps":[{"text_pt":"Coeficiente de doacoes na regressão within: votos e doacoes menos a média de cada municipio; igual à regressão com uma dummy por unidade.","formula":"","terms":[{"symbol":"unidades","value":80.0,"unit":"","note":""},{"symbol":"n","value":640.0,"unit":"observações","note":""},{"symbol":"k","value":81.0,"unit":"parâmetros, médias das unidades incluídas","note":""}],"result":0.014083451691745961,"unit":""},{"text_pt":"Erro-padrão robusto por conglomerado (CR1): a raiz da variância estimada do coeficiente.","formula":"ep = √V_jj","terms":[{"symbol":"V_jj","value":2.2261515830806293e-8,"unit":"","note":"diagonal da matriz de covariância"}],"result":0.00014920293506096418,"unit":""},{"text_pt":"Intervalo de 95% de confiança: a estimativa mais ou menos o valor crítico da distribuição t de Student com 79 graus de liberdade vezes o erro-padrão.","formula":"IC = β ± c · ep","terms":[{"symbol":"β","value":0.014083451691745961,"unit":"","note":""},{"symbol":"c","value":1.9904502102301287,"unit":"","note":"quantil 0.975"},{"symbol":"ep","value":0.00014920293506096418,"unit":"","note":""}],"result":null,"unit":""}],"note_pt":""}}],"diagnostics":[{"name":"n_units","value":80,"flag":false,"note":""},{"name":"n_obs","value":640,"flag":false,"note":""},{"name":"k","value":81,"flag":false,"note":"parameters of the equivalent dummy regression (unit means included)"},{"name":"r2_within","value":0.9485321600978223,"flag":false,"note":""},{"name":"within_sd.doacoes","value":2.790895296775331,"flag":false,"note":"SD of the regressor after removing unit means: the variation the estimate uses"},{"name":"sign_flip","value":true,"flag":true,"note":"pooled and within slopes have opposite signs for ['doacoes']: units with more of the regressor differ for other reasons (Simpson's paradox); the pooled slope is confounded"},{"name":"se_type","value":"cluster","flag":false,"note":"cluster-robust standard errors (CR1) by 'municipio'"},{"name":"n_clusters","value":80,"flag":false,"note":"80 clusters of 'municipio'"}],"tables":{"pooled_vs_within":[{"regressor":"doacoes","pooled":-0.022039130676803336,"pooled_se":0.0009193303241380859,"within":0.014083451691745961,"within_se":0.00014920293506096418,"sd":7.922069626834988,"within_sd":2.790895296775331}]},"datasets":{},"warnings":[],"assumptions":["unobserved differences between units are constant over time","the regressors are strictly exogenous","errors are independent across clusters of 'municipio' and may be correlated within them"],"notes":""},"outputs":null,"output_digest":"b68980df98d9c58a4fd1cc0deb64db5969c5c1f7fcd82b92baaf9ba0cf0457eb","params_versions":[],"sources":[],"started_at":"2026-10-15T17:14:02.495117Z","duration_ms":37.0685001835227,"error":null,"decided_by":"model","rationale":"As mesmas unidades ao longo do tempo: efeitos fixos de unidade.","cached":false,"informed_by":[],"model":null,"prompt":null,"response":null,"tokens_in":null,"tokens_out":null,"meta":{"turn_id":"call_66606758","llm_name":"infer__fe","vendor_id":"baseline_1","result_for_model_ref":"f63e5b6676576d9c913a3a344603a4219a1cc7d3767075b42abe75a0b1c93c61"}}
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