{"attempt":1,"datasets":{"ds_0073":{"description_shown":"| Variable Name | Description                                                                                             |\n|---------------|---------------------------------------------------------------------------------------------------------|\n| Unnamed: 0    | Row identifier carried over from the source file (sequential index).                                    |\n| dv            | Party\u2019s general-election two-party vote share in the district-year (0\u20131 scale).                         |\n| rv            | Extremist primary candidate\u2019s vote-share margin among the top-two primary candidates (share \u2212 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\u2013party\u2013year observation in which an ideologically \u201cextreme\u201d primary candidate (defined relative to their primary opponent using contribution-based ideology scores) is identified, along with that candidate\u2019s primary margin (centered at 0.5) and the party\u2019s 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","file_sha256":"cc8fd9c994a1f10515841b742e28d4298e17851dd1fe2104c8b8c64098389891","frame_sha256":null,"role":"agent","transforms":[]}},"epoch":1,"event":"sample_init","question":"Among 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?","question_sha256":"c8cd2897dcc6585ab11c4c0bd9bb846839ea1a2e88cd25377d21dd11943097f8","run_id":"20261015T171027Z-suite-crb_papers-naive-54c3f2","seq":0,"skills":[],"task_id":"crb_papers.q107","task_sha256":"62529fc0d713158f0b253fc09bd3343cd2a621348603e3e811db88d89b4e6f3f","timestamp":"2026-10-15T17:10:30.397755Z"}
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{"arguments":{"cluster":null,"controls":[],"dataset":"ds_0073","fe":[],"intercept":true,"level":0.95,"se":"HC1","subset":null,"weights":null,"x":["rv"],"y":"dv"},"attempt":1,"cached":false,"call_id":"call_9a48d40f","card_version":"0.1.0","code_version":"2925aeab2262","dataset_digests":{"ds_0073":"cb1250885239a207a7946b0867d2129db5e4f868971916f1ba2b8e661e3f8a9d"},"decided_by":"model","duration_ms":1.8677501939237118,"epoch":1,"error":null,"event":"tool","informed_by":[],"input_digest":"2a2f46446324d518be332b183b4aaea907adf9ff20c1196a149f74887aa2593c","llm_name":"infer__ols","output_digest":"d1b1c826f569597a8d2bcce47dd11d1ee3011e9a972a0fa03ca58d402a87c111","rationale":"Linha de base ing\u00eanua: regress\u00e3o do resultado no tratamento, sem controles.","replay_class":"replayed","result":{"assumptions":["the conditional mean of the outcome is linear in the regressors","errors are independent across observations; their variance may differ"],"call_id":"call_9a48d40f","datasets":{},"diagnostics":[{"flag":false,"name":"n_obs","note":"observations used","value":252},{"flag":false,"name":"k","note":"parameters estimated, fixed-effect dummies included","value":2},{"flag":false,"name":"r2","note":"","value":7.663517406264475e-05},{"flag":false,"name":"df_resid","note":"","value":250},{"flag":false,"name":"se_type","note":"heteroskedasticity-robust standard errors (HC1)","value":"HC1"},{"flag":false,"name":"n_clusters","note":"errors not clustered","value":null}],"figures":[{"ci_high":0.14625742440741757,"ci_level":0.95,"ci_low":-0.1672359409440386,"derivation":{"note_pt":"","steps":[{"formula":"","result":-0.01048925826831051,"terms":[{"note":"","symbol":"n","unit":"observa\u00e7\u00f5es","value":252.0},{"note":"","symbol":"k","unit":"par\u00e2metros","value":2.0}],"text_pt":"Coeficiente de rv na regress\u00e3o por m\u00ednimos quadrados ordin\u00e1rios de dv.","unit":""},{"formula":"ep = \u221aV_jj","result":0.07958710867666584,"terms":[{"note":"diagonal da matriz de covari\u00e2ncia","symbol":"V_jj","unit":"","value":0.006334107867511419}],"text_pt":"Erro-padr\u00e3o robusto \u00e0 heterocedasticidade (HC1): a raiz da vari\u00e2ncia estimada do coeficiente.","unit":""},{"formula":"IC = \u03b2 \u00b1 c \u00b7 ep","result":null,"terms":[{"note":"","symbol":"\u03b2","unit":"","value":-0.01048925826831051},{"note":"quantil 0.975","symbol":"c","unit":"","value":1.9694983934211532},{"note":"","symbol":"ep","unit":"","value":0.07958710867666584}],"text_pt":"Intervalo de 95% de confian\u00e7a: a estimativa mais ou menos o valor cr\u00edtico da distribui\u00e7\u00e3o t de Student com 250 graus de liberdade vezes o erro-padr\u00e3o.","unit":""}]},"key":"coef.rv","label":"Coefficient on rv in the regression of dv","se":0.07958710867666584,"unit":"","value":-0.01048925826831051}],"notes":"","reason":"","status":"ok","tables":{"coefficients":[{"ci_high":0.6120019666156903,"ci_low":0.5772148018113864,"coef":0.5946083842135383,"p":3.111166787233128e-162,"se":0.008831478339994044,"t":67.32829559472592,"term":"Intercept"},{"ci_high":0.14625742440741757,"ci_low":-0.1672359409440386,"coef":-0.01048925826831051,"p":0.8952516987792173,"se":0.07958710867666584,"t":-0.13179594588521418,"term":"rv"}]},"tool":"infer.ols","version":"0.1.0","warnings":[]},"result_for_model_ref":"04c430fe6ecae1bcfbba8bfbf59b1530fa8b1a44db37b110c816f87474ad191a","result_sha256":"096f649385ff6bc05b67d53fa87a11b0fc3c4d71aee0ecfe8b78f1c53926b7bd","run_id":"20261015T171027Z-suite-crb_papers-naive-54c3f2","seq":2,"synthetic":false,"task_id":"crb_papers.q107","timestamp":"2026-10-15T17:10:30.400962Z","tool_id":"infer.ols","turn_id":"call_80158c1a"}
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