{"call_id":"call_80158c1a","run_id":"20261015T171027Z-suite-crb_papers-naive-54c3f2/crb_papers.q107/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":"2026-10-15T17:10:30.397755Z","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\": \"ds_0073\",\n \"rows\": 252,\n \"description\": \"| Variable Name | Description                                                                                             |\\n|---------------|---------------------------------------------------------------------------------------------------------|\\n| Unnamed: 0    | Row identifier carried over from the source file (sequential index).                                    |\\n| dv            | Party’s general-election two-party vote share in the district-year (0–1 scale).                         |\\n| rv            | Extremist primary candidate’s vote-share margin among the top-two primary candidates (share − 0.5); negative values indicate the extremist narrowly lost the primary, positive values indicate a narrow win. |\\n| dv_win        | Indicator for whether the party won the subsequent general election (1 = win, 0 = loss).                |\\n\\nData Description:  \\nThis dataset is built from U.S. House elections between 1980 and 2010 and links close primary contests to subsequent general-election performance at the district level. The sample focuses on contested primaries featuring two viable candidates for whom ideological positions could be estimated using patterns of primary-election campaign receipts. Election results were compiled from election-return records, with general-election outcomes recorded as vote share and win/loss indicators.\\n\\nEach row reflects a district–party–year observation in which an ideologically “extreme” primary candidate (defined relative to their primary opponent using contribution-based ideology scores) is identified, along with that candidate’s primary margin (centered at 0.5) and the party’s subsequent general-election performance. The data provide concise measures needed to examine how primary nomination contexts relate to general-election results, using standardized constructs such as two-party vote share, a binary win indicator, and a centered primary vote margin for the candidate classified as extreme.\\n\",\n \"columns\": {\n  \"Unnamed: 0\": \"int64\",\n  \"dv\": \"float64\",\n  \"rv\": \"float64\",\n  \"dv_win\": \"int64\"\n }\n}\n\n# Question\nAmong U.S. House primaries decided by a razor-thin margin between an extremist and a more moderate candidate, what is the average causal effect of nominating the extremist on the party's general-election vote share?","response":"Linha de base ingênua: regressão do resultado no tratamento, sem controles.\ninfer__ols({\"dataset\": \"ds_0073\", \"y\": \"dv\", \"x\": [\"rv\"]})","tokens_in":0,"tokens_out":0,"meta":{"turn_call_id":"call_80158c1a","text":"Linha de base ingênua: regressão do resultado no tratamento, sem controles.","tool_calls":[{"vendor_id":"script_1","llm_name":"infer__ols","tool_id":"infer.ols","args":{"dataset":"ds_0073","y":"dv","x":["rv"]}}],"request":{"system_stable_ref":"433a6877aa6a378bbd4b7e2752e484d8cb7499c562d68222be231f62ff2fa420","system_task_ref":"c7a12e54f3aee545babfb19aa688dc1fa24dd0ee9f6b573f58725d0ec6306e39","tools_ref":"52ab03d8ec104abb85572294bf8a469d75c5de9e74d6042caabff8017adc572b","contents_refs":[],"messages_refs":["9df2da51ecf3dba247ccb2fee904e53fc1c5fea3dae9010008e09f8fe569d0ab"],"config":{}},"attempts":[],"response_id":null,"finish_reason":"tool_use","model_reported":"naive-ols","usage":{"input":0,"output":0,"thoughts":0,"cached":0},"thoughts":null,"vendor":false,"wall_ms":0.9596669115126133}}
{"call_id":"call_9a48d40f","run_id":"20261015T171027Z-suite-crb_papers-naive-54c3f2/crb_papers.q107/e1a1","step":"analysis","kind":"tool","name":"infer.ols","version":"0.1.0","code_version":"2925aeab2262","inputs":{"dataset":"ds_0073","y":"dv","x":["rv"],"controls":[],"fe":[],"cluster":null,"se":"HC1","weights":null,"subset":null,"intercept":true,"level":0.95},"input_digest":"2a2f46446324d518be332b183b4aaea907adf9ff20c1196a149f74887aa2593c","dataset_digests":{"ds_0073":"cb1250885239a207a7946b0867d2129db5e4f868971916f1ba2b8e661e3f8a9d"},"result":{"tool":"infer.ols","version":"0.1.0","call_id":"call_9a48d40f","status":"ok","reason":"","figures":[{"key":"coef.rv","value":-0.01048925826831051,"unit":"","label":"Coefficient on rv in the regression of dv","se":0.07958710867666584,"ci_low":-0.1672359409440386,"ci_high":0.14625742440741757,"ci_level":0.95,"derivation":{"steps":[{"text_pt":"Coeficiente de rv na regressão por mínimos quadrados ordinários de dv.","formula":"","terms":[{"symbol":"n","value":252.0,"unit":"observações","note":""},{"symbol":"k","value":2.0,"unit":"parâmetros","note":""}],"result":-0.01048925826831051,"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":0.006334107867511419,"unit":"","note":"diagonal da matriz de covariância"}],"result":0.07958710867666584,"unit":""},{"text_pt":"Intervalo de 95% de confiança: a estimativa mais ou menos o valor crítico da distribuição t de Student com 250 graus de liberdade vezes o erro-padrão.","formula":"IC = β ± c · ep","terms":[{"symbol":"β","value":-0.01048925826831051,"unit":"","note":""},{"symbol":"c","value":1.9694983934211532,"unit":"","note":"quantil 0.975"},{"symbol":"ep","value":0.07958710867666584,"unit":"","note":""}],"result":null,"unit":""}],"note_pt":""}}],"diagnostics":[{"name":"n_obs","value":252,"flag":false,"note":"observations used"},{"name":"k","value":2,"flag":false,"note":"parameters estimated, fixed-effect dummies included"},{"name":"r2","value":0.00007663517406264475,"flag":false,"note":""},{"name":"df_resid","value":250,"flag":false,"note":""},{"name":"se_type","value":"HC1","flag":false,"note":"heteroskedasticity-robust standard errors (HC1)"},{"name":"n_clusters","value":null,"flag":false,"note":"errors not clustered"}],"tables":{"coefficients":[{"term":"Intercept","coef":0.5946083842135383,"se":0.008831478339994044,"t":67.32829559472592,"p":3.111166787233128e-162,"ci_low":0.5772148018113864,"ci_high":0.6120019666156903},{"term":"rv","coef":-0.01048925826831051,"se":0.07958710867666584,"t":-0.13179594588521418,"p":0.8952516987792173,"ci_low":-0.1672359409440386,"ci_high":0.14625742440741757}]},"datasets":{},"warnings":[],"assumptions":["the conditional mean of the outcome is linear in the regressors","errors are independent across observations; their variance may differ"],"notes":""},"outputs":null,"output_digest":"d1b1c826f569597a8d2bcce47dd11d1ee3011e9a972a0fa03ca58d402a87c111","params_versions":[],"sources":[],"started_at":"2026-10-15T17:10:30.400962Z","duration_ms":1.8677501939237118,"error":null,"decided_by":"model","rationale":"Linha de base ingênua: regressão do resultado no tratamento, sem controles.","cached":false,"informed_by":[],"model":null,"prompt":null,"response":null,"tokens_in":null,"tokens_out":null,"meta":{"turn_id":"call_80158c1a","llm_name":"infer__ols","vendor_id":"script_1","result_for_model_ref":"04c430fe6ecae1bcfbba8bfbf59b1530fa8b1a44db37b110c816f87474ad191a"}}
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