{"call_id":"call_c50bb85d","run_id":"20261015T165911Z-suite-paper_repro-baseline-8b4f85/paper_repro.lmb_2004.tab1_c3.design/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":"2026-10-15T16:59:22.593376Z","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\": \"lmb\",\n \"rows\": 13588,\n \"description\": \"U.S. House elections and roll-call voting, the extract of the data of Lee, Moretti and Butler (2004), 'Do Voters Affect or Elect Policies? Evidence from the U.S. House', distributed with the causaldata R package: one row per district and congressional session (13,588 rows). Each row pairs a district's election with the one before it for the same seat. Columns: rownames, the row number; state, ICPSR state code; district, district code; id, an election identifier; score, the adjusted ADA voting score of the district's representative in the session that follows the row's election (0 to 100, higher means a more liberal roll-call record); year, year of the election; demvoteshare, the Democratic candidate's share of the two-party vote in the row's election; democrat, 1 if the Democratic candidate won the row's election; lagdemvoteshare, the Democratic share of the two-party vote in the previous election for the same seat; lagdemocrat, 1 if the Democratic candidate won that previous election. In the paper's notation the previous election is time t and the row's election, and the session after it, are time t+1.\",\n \"columns\": {\n  \"rownames\": \"int64\",\n  \"state\": \"int64\",\n  \"district\": \"int64\",\n  \"id\": \"int64\",\n  \"score\": \"float64\",\n  \"year\": \"int64\",\n  \"demvoteshare\": \"float64\",\n  \"democrat\": \"int64\",\n  \"lagdemocrat\": \"int64\",\n  \"lagdemvoteshare\": \"float64\"\n }\n}\n\n# Question\nHow large is the incumbent party's advantage in U.S. House elections? Comparing seats the Democratic Party barely won with seats it barely lost, estimate the effect of winning a House election on the probability that the same party wins the seat's next election.","response":"O tratamento muda num limiar da variável de corte: regressão descontínua.\ninfer__rdd({\"dataset\": \"lmb\", \"y\": \"rownames\", \"running\": \"score\", \"cutoff\": 0.0})","tokens_in":0,"tokens_out":0,"meta":{"turn_call_id":"call_c50bb85d","text":"O tratamento muda num limiar da variável de corte: regressão descontínua.","tool_calls":[{"vendor_id":"baseline_1","llm_name":"infer__rdd","tool_id":"infer.rdd","args":{"dataset":"lmb","y":"rownames","running":"score","cutoff":0.0}}],"request":{"system_stable_ref":"433a6877aa6a378bbd4b7e2752e484d8cb7499c562d68222be231f62ff2fa420","system_task_ref":"b464788742358119415ce56dc044de30838474cd0ce71792d48a4fd545c86f9f","tools_ref":"52ab03d8ec104abb85572294bf8a469d75c5de9e74d6042caabff8017adc572b","contents_refs":[],"messages_refs":["cd808089ea5f16af8267c2b8b8431d7e98521c987066730b15f6e24886af47b8"],"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":1.869875006377697}}
{"call_id":"call_965d46cf","run_id":"20261015T165911Z-suite-paper_repro-baseline-8b4f85/paper_repro.lmb_2004.tab1_c3.design/e1a1","step":"analysis","kind":"tool","name":"infer.rdd","version":"0.1.0","code_version":"62cdbe0860ad","inputs":{"dataset":"lmb","y":"rownames","running":"score","cutoff":0.0,"bandwidth":null,"kernel":"triangular","polynomial":1,"cluster":null,"subset":null,"level":0.95},"input_digest":"0035c3a42f732c61b17c8be799e995385e4630fa9d8bea11692ae96f0f976c5f","dataset_digests":{"lmb":"6f2fbc93d91c4156cab244ba7581cc8eae45210626f97f85ac8b2d366e4cbc20"},"result":{"tool":"infer.rdd","version":"0.1.0","call_id":"call_965d46cf","status":"ok","reason":"","figures":[{"key":"effect","value":279.16473129771043,"unit":"","label":"Jump in rownames at the cutoff of score","se":248.25990671589568,"ci_low":-207.4157446707186,"ci_high":765.7452072661395,"ci_level":0.95,"derivation":{"steps":[{"text_pt":"Ajuste linear ponderado (kernel triangular) de cada lado do limiar, com janela h (Imbens–Kalyanaraman); o efeito é a diferença entre os dois ajustes no limiar.","formula":"","terms":[{"symbol":"h","value":14.208597376322317,"unit":"","note":"janela (Imbens–Kalyanaraman)"},{"symbol":"n_esq","value":758.0,"unit":"observações abaixo do limiar","note":""},{"symbol":"n_dir","value":3180.0,"unit":"observações acima do limiar","note":""}],"result":279.16473129771043,"unit":""},{"text_pt":"Erro-padrão robusto à heterocedasticidade (HC1): a raiz da variância estimada do coeficiente.","formula":"ep = √V_jj","terms":[{"symbol":"V_jj","value":61632.98128258523,"unit":"","note":"diagonal da matriz de covariância"}],"result":248.25990671589568,"unit":""},{"text_pt":"Intervalo de 95% de confiança: a estimativa mais ou menos o valor crítico da distribuição normal vezes o erro-padrão.","formula":"IC = τ ± c · ep","terms":[{"symbol":"τ","value":279.16473129771043,"unit":"","note":""},{"symbol":"c","value":1.959963984540054,"unit":"","note":"quantil 0.975"},{"symbol":"ep","value":248.25990671589568,"unit":"","note":""}],"result":null,"unit":""}],"note_pt":""}}],"diagnostics":[{"name":"bandwidth","value":14.208597376322317,"flag":false,"note":"half-width of the window around the cutoff (Imbens–Kalyanaraman)"},{"name":"bandwidth_method","value":"IK","flag":false,"note":""},{"name":"n_left","value":758,"flag":false,"note":"observations with positive weight below the cutoff"},{"name":"n_right","value":3180,"flag":false,"note":"observations with positive weight at or above the cutoff"},{"name":"kernel","value":"triangular","flag":false,"note":""},{"name":"polynomial","value":1,"flag":false,"note":""},{"name":"bandwidth_sensitive","value":true,"flag":true,"note":"the estimate at half or double the bandwidth falls outside the main interval: the result depends on the window"},{"name":"n_obs","value":13588,"flag":false,"note":"rows used to choose the bandwidth"},{"name":"se_type","value":"HC1","flag":false,"note":"heteroskedasticity-robust standard errors (HC1)"}],"tables":{"sensitivity":[{"multiplier":0.5,"bandwidth":7.104298688161158,"estimate":-527.5201994302511,"se":358.26323437260737,"ci_low":-1229.7032357853939,"ci_high":174.66283692489174,"n_left":575,"n_right":1459},{"multiplier":1.0,"bandwidth":14.208597376322317,"estimate":279.16473129771043,"se":248.25990671589568,"ci_low":-207.4157446707186,"ci_high":765.7452072661395,"n_left":758,"n_right":3180},{"multiplier":2.0,"bandwidth":28.417194752644633,"estimate":487.3436579894338,"se":199.57092247800273,"ci_low":96.1918375711133,"ci_high":878.4954784077543,"n_left":819,"n_right":5048}]},"datasets":{},"warnings":[],"assumptions":["units cannot precisely control the running variable around the cutoff","every other determinant of the outcome is continuous at the cutoff"],"notes":""},"outputs":null,"output_digest":"72d098165c2430a47c048c0b6a43e948545ebe153d709b57e1f7d82ae4fcfc28","params_versions":[],"sources":[],"started_at":"2026-10-15T16:59:22.598084Z","duration_ms":9.733333019539714,"error":null,"decided_by":"model","rationale":"O tratamento muda num limiar da variável de corte: regressão descontínua.","cached":false,"informed_by":[],"model":null,"prompt":null,"response":null,"tokens_in":null,"tokens_out":null,"meta":{"turn_id":"call_c50bb85d","llm_name":"infer__rdd","vendor_id":"baseline_1","result_for_model_ref":"182e2d0093cd4622a4a8de228c73d03055cc983c684383dc97aaf47a42764652"}}
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