{"call_id":"call_fd35e019","run_id":"20261015T165744Z-suite-paper_repro-scripted-def9df/paper_repro.ccft_2017.senate_h16794.design/e1a1","step":"analysis","kind":"llm","name":"llm.analyst.scripted","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":"2026-10-15T16:57:55.335012Z","duration_ms":0.0,"error":null,"decided_by":"model","rationale":"","cached":false,"informed_by":[],"model":"reference","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\": \"senate\",\n \"rows\": 1390,\n \"description\": \"U.S. Senate elections, 1914-2010, the extract of the data of Cattaneo, Frandsen and Titiunik (2015) that ships with the rdrobust package as its illustration: one row per state and Senate election (1,390 rows). Columns: state, state code; year, year of the election; margin, the Democratic party's margin of victory in that election, in percentage points (negative when the Democratic candidate lost); vote, the Democratic party's vote share, in percent, in the next election for the same Senate seat (missing for some rows); class, the seat's Senate class (1, 2 or 3); termshouse and termssenate, terms the Democratic candidate had served in the House and in the Senate; population, the state's population.\",\n \"columns\": {\n  \"state\": \"int64\",\n  \"year\": \"int64\",\n  \"vote\": \"float64\",\n  \"margin\": \"float64\",\n  \"class\": \"int64\",\n  \"termshouse\": \"float64\",\n  \"termssenate\": \"float64\",\n  \"population\": \"int64\"\n }\n}\n\n# Question\nIs there an incumbency advantage in U.S. Senate elections? Comparing seats the Democratic party barely won with seats it barely lost, estimate the effect of winning a Senate election on the Democratic party's share of the votes in the next election for the same seat.","response":"rdrobust's Senate example at the bandwidth h = 16.79369 that Calonico, Cattaneo and Titiunik (2014) selected: local linear, triangular kernel, cutoff 0; the conventional point estimate (rdrobust's own errors are nearest-neighbour based; the tool's are HC1).\ninfer__rdd({\"dataset\": \"senate\", \"y\": \"vote\", \"running\": \"margin\", \"cutoff\": 0, \"bandwidth\": 16.79369, \"kernel\": \"triangular\", \"polynomial\": 1})","tokens_in":0,"tokens_out":0,"meta":{"turn_call_id":"call_fd35e019","text":"rdrobust's Senate example at the bandwidth h = 16.79369 that Calonico, Cattaneo and Titiunik (2014) selected: local linear, triangular kernel, cutoff 0; the conventional point estimate (rdrobust's own errors are nearest-neighbour based; the tool's are HC1).","tool_calls":[{"vendor_id":"script_1","llm_name":"infer__rdd","tool_id":"infer.rdd","args":{"dataset":"senate","y":"vote","running":"margin","cutoff":0,"bandwidth":16.79369,"kernel":"triangular","polynomial":1}}],"request":{"system_stable_ref":"433a6877aa6a378bbd4b7e2752e484d8cb7499c562d68222be231f62ff2fa420","system_task_ref":"311a3753b72e2040464f460749902284aa340d0bc673d39ccc2bb85cf7cf846e","tools_ref":"52ab03d8ec104abb85572294bf8a469d75c5de9e74d6042caabff8017adc572b","contents_refs":[],"messages_refs":["2b0988f39998ba57b4f4c4a2a6533716530cd6550b82e5c9bb41e43ee451f3fc"],"config":{}},"attempts":[],"response_id":null,"finish_reason":"tool_use","model_reported":"reference","usage":{"input":0,"output":0,"thoughts":0,"cached":0},"thoughts":null,"vendor":false,"wall_ms":0.8206248749047518}}
{"call_id":"call_e120760b","run_id":"20261015T165744Z-suite-paper_repro-scripted-def9df/paper_repro.ccft_2017.senate_h16794.design/e1a1","step":"analysis","kind":"tool","name":"infer.rdd","version":"0.1.0","code_version":"62cdbe0860ad","inputs":{"dataset":"senate","y":"vote","running":"margin","cutoff":0.0,"bandwidth":16.79369,"kernel":"triangular","polynomial":1,"cluster":null,"subset":null,"level":0.95},"input_digest":"1b4ee36280571b84e65beb507980197582b494159b4027daea876d50dde7e599","dataset_digests":{"senate":"e1181ed0f741b1485e582b1110d74d93e57241b2b9bfa1ed59f2cd80ab0b165e"},"result":{"tool":"infer.rdd","version":"0.1.0","call_id":"call_e120760b","status":"ok","reason":"","figures":[{"key":"effect","value":7.425304341122355,"unit":"","label":"Jump in vote at the cutoff of margin","se":1.497901791714546,"ci_low":4.489470776983827,"ci_high":10.361137905260883,"ci_level":0.95,"derivation":{"steps":[{"text_pt":"Ajuste linear ponderado (kernel triangular) de cada lado do limiar, com janela h (informada); o efeito é a diferença entre os dois ajustes no limiar.","formula":"","terms":[{"symbol":"h","value":16.79369,"unit":"","note":"janela (informada)"},{"symbol":"n_esq","value":343.0,"unit":"observações abaixo do limiar","note":""},{"symbol":"n_dir","value":310.0,"unit":"observações acima do limiar","note":""}],"result":7.425304341122355,"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":2.2437097776216475,"unit":"","note":"diagonal da matriz de covariância"}],"result":1.497901791714546,"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":7.425304341122355,"unit":"","note":""},{"symbol":"c","value":1.959963984540054,"unit":"","note":"quantil 0.975"},{"symbol":"ep","value":1.497901791714546,"unit":"","note":""}],"result":null,"unit":""}],"note_pt":""}}],"diagnostics":[{"name":"bandwidth","value":16.79369,"flag":false,"note":"half-width of the window around the cutoff (informada)"},{"name":"bandwidth_method","value":"user","flag":false,"note":""},{"name":"n_left","value":343,"flag":false,"note":"observations with positive weight below the cutoff"},{"name":"n_right","value":310,"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":false,"flag":false,"note":"the estimates at half and double the bandwidth fall inside the main interval"},{"name":"n_obs","value":1297,"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":8.396845,"estimate":8.755363267228034,"se":1.9948448719814,"ci_low":4.845539163400076,"ci_high":12.665187371055993,"n_left":217,"n_right":175},{"multiplier":1.0,"bandwidth":16.79369,"estimate":7.425304341122355,"se":1.497901791714546,"ci_low":4.489470776983827,"ci_high":10.361137905260883,"n_left":343,"n_right":310},{"multiplier":2.0,"bandwidth":33.58738,"estimate":7.253397244991841,"se":1.1234745949426124,"ci_low":5.051427501358595,"ci_high":9.455366988625087,"n_left":499,"n_right":488}]},"datasets":{},"warnings":["93 rows were dropped because one of 'vote', 'margin' was missing"],"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":"8829b4413996a31cd5eb1b301fccdc1d87f5b65ec5a660fcd578b6b6dcb3532c","params_versions":[],"sources":[],"started_at":"2026-10-15T16:57:55.337484Z","duration_ms":8.855750085785985,"error":null,"decided_by":"model","rationale":"rdrobust's Senate example at the bandwidth h = 16.79369 that Calonico, Cattaneo and Titiunik (2014) selected: local linear, triangular kernel, cutoff 0; the conventional point estimate (rdrobust's own errors are nearest-neighbour based; the tool's are HC1).","cached":false,"informed_by":[],"model":null,"prompt":null,"response":null,"tokens_in":null,"tokens_out":null,"meta":{"turn_id":"call_fd35e019","llm_name":"infer__rdd","vendor_id":"script_1","result_for_model_ref":"b6dafd3d0e86c572298e3dbd27c7c3ff2273f2479676df03d46d9f6537038bd0"}}
{"call_id":"call_1282a9cb","run_id":"20261015T165744Z-suite-paper_repro-scripted-def9df/paper_repro.ccft_2017.senate_h16794.design/e1a1","step":"analysis","kind":"llm","name":"llm.analyst.scripted","version":"","code_version":"62cdbe0860ad","inputs":{},"input_digest":"","dataset_digests":{},"result":null,"outputs":null,"output_digest":"","params_versions":[],"sources":[],"started_at":"2026-10-15T16:57:55.346541Z","duration_ms":0.0,"error":null,"decided_by":"model","rationale":"","cached":false,"informed_by":[],"model":"reference","prompt":null,"response":"submit_answer({\"answer\": \"Estimativa (infer.rdd): effect = 7,425.\", \"figure_refs\": [\"call_e120760b:effect\"], \"caveats\": []})","tokens_in":0,"tokens_out":0,"meta":{"turn_call_id":"call_1282a9cb","text":"","tool_calls":[{"vendor_id":"script_2","llm_name":"submit_answer","tool_id":null,"args":{"answer":"Estimativa (infer.rdd): effect = 7,425.","figure_refs":["call_e120760b:effect"],"caveats":[]}}],"request":{"system_stable_ref":"433a6877aa6a378bbd4b7e2752e484d8cb7499c562d68222be231f62ff2fa420","system_task_ref":"311a3753b72e2040464f460749902284aa340d0bc673d39ccc2bb85cf7cf846e","tools_ref":"52ab03d8ec104abb85572294bf8a469d75c5de9e74d6042caabff8017adc572b","contents_refs":[],"messages_refs":["2b0988f39998ba57b4f4c4a2a6533716530cd6550b82e5c9bb41e43ee451f3fc","1c70bea91a25b2be2189a3a6f328fd67cc868de736275b57661aa4c4b7bb6ff2","0bd2d668612f6dbbc1c67eda4f7ad45f95ed96ddf75a0dacd90501181c5bee6e"],"config":{}},"attempts":[],"response_id":null,"finish_reason":"tool_use","model_reported":"reference","usage":{"input":0,"output":0,"thoughts":0,"cached":0},"thoughts":null,"vendor":false,"wall_ms":0.2429590094834566}}
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