{"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 districts at the margin where a more extreme candidate just wins versus just loses the primary, what is the average causal effect of nominating the extremist (versus a relatively moderate candidate) on the party's probability of winning the subsequent general election?","question_sha256":"7bfaad9e2f801fd04a34a9fdc128ee92a3b7137c9ee3975b7f778bf83b246c83","run_id":"20261015T171027Z-suite-crb_papers-naive-54c3f2","seq":0,"skills":[],"task_id":"crb_papers.q108","task_sha256":"99f9c1bd3698b7e5c5c4f1d2e5a55b0d1c2299ab6d7b9de32253596ff9218780","timestamp":"2026-10-15T17:10:30.412340Z"}
{"attempt":1,"attempts":[],"call_id":"call_0f14a2dc","epoch":1,"event":"model","finish_reason":"tool_use","latency_ms":0.0,"model_reported":"naive-ols","model_requested":"naive-ols","provider":"scripted","request":{"config":{},"contents_refs":[],"messages_refs":["0d4bd06de2de311901190519ca0b0e85ea7c50ba81f89914252a4f618f646ac0"],"system_stable_ref":"433a6877aa6a378bbd4b7e2752e484d8cb7499c562d68222be231f62ff2fa420","system_task_ref":"c7a12e54f3aee545babfb19aa688dc1fa24dd0ee9f6b573f58725d0ec6306e39","tools_ref":"52ab03d8ec104abb85572294bf8a469d75c5de9e74d6042caabff8017adc572b"},"response":{"text":"Linha de base ing\u00eanua: regress\u00e3o do resultado no tratamento, sem controles.","thoughts":null,"tool_calls":[{"args":{"dataset":"ds_0073","x":["rv"],"y":"dv_win"},"llm_name":"infer__ols","tool_id":"infer.ols","vendor_id":"script_1"}]},"response_id":null,"run_id":"20261015T171027Z-suite-crb_papers-naive-54c3f2","seq":1,"task_id":"crb_papers.q108","timestamp":"2026-10-15T17:10:30.412340Z","usage":{"cached":0,"input":0,"output":0,"thoughts":0},"vendor":false}
{"arguments":{"cluster":null,"controls":[],"dataset":"ds_0073","fe":[],"intercept":true,"level":0.95,"se":"HC1","subset":null,"weights":null,"x":["rv"],"y":"dv_win"},"attempt":1,"cached":false,"call_id":"call_ee705585","card_version":"0.1.0","code_version":"2925aeab2262","dataset_digests":{"ds_0073":"cb1250885239a207a7946b0867d2129db5e4f868971916f1ba2b8e661e3f8a9d"},"decided_by":"model","duration_ms":2.2538751363754272,"epoch":1,"error":null,"event":"tool","informed_by":[],"input_digest":"d207bb1f4b2a392956e60383299bb9da8f53950bf02bb27266b1f6fb34d0649d","llm_name":"infer__ols","output_digest":"dec0e52f7f028f4e130dd33c320b6772970850121ad86813dbc91e7b05094758","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_ee705585","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":0.008860137638568966},{"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":0.07975763136194047,"ci_level":0.95,"ci_low":-0.7892676123999026,"derivation":{"note_pt":"","steps":[{"formula":"","result":-0.3547549905189811,"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_win.","unit":""},{"formula":"ep = \u221aV_jj","result":0.22062095776891874,"terms":[{"note":"diagonal da matriz de covari\u00e2ncia","symbol":"V_jj","unit":"","value":0.04867360700687503}],"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":-0.3547549905189811},{"note":"quantil 0.975","symbol":"c","unit":"","value":1.9694983934211532},{"note":"","symbol":"ep","unit":"","value":0.22062095776891874}],"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_win","se":0.22062095776891874,"unit":"","value":-0.3547549905189811}],"notes":"","reason":"","status":"ok","tables":{"coefficients":[{"ci_high":0.7967857239449954,"ci_low":0.688774599379649,"coef":0.7427801616623222,"p":2.4939979572837613e-76,"se":0.027420973006665836,"t":27.088030810641104,"term":"Intercept"},{"ci_high":0.07975763136194047,"ci_low":-0.7892676123999026,"coef":-0.3547549905189811,"p":0.10910079463842284,"se":0.22062095776891874,"t":-1.6079840922935167,"term":"rv"}]},"tool":"infer.ols","version":"0.1.0","warnings":[]},"result_for_model_ref":"ab90105617f4bc105b69b061788e8c865b152f4b63c30250c3de50a5bd3f1d97","result_sha256":"0ad9d61e2d9e431d054861f41170dae859c0372e15f790faa9e4e46ff75c9282","run_id":"20261015T171027Z-suite-crb_papers-naive-54c3f2","seq":2,"synthetic":false,"task_id":"crb_papers.q108","timestamp":"2026-10-15T17:10:30.414839Z","tool_id":"infer.ols","turn_id":"call_0f14a2dc"}
{"attempt":1,"attempts":[],"call_id":"call_fbfb9923","epoch":1,"event":"model","finish_reason":"tool_use","latency_ms":0.0,"model_reported":"naive-ols","model_requested":"naive-ols","provider":"scripted","request":{"config":{},"contents_refs":[],"messages_refs":["0d4bd06de2de311901190519ca0b0e85ea7c50ba81f89914252a4f618f646ac0","904870251a20e6bb477797f5e1178ac690a4ef12395df760e75875897a34104b","da139b57a3ced2d824f4477038f0fe4a9be7a011dd2f74e60adc70fa09195943"],"system_stable_ref":"433a6877aa6a378bbd4b7e2752e484d8cb7499c562d68222be231f62ff2fa420","system_task_ref":"c7a12e54f3aee545babfb19aa688dc1fa24dd0ee9f6b573f58725d0ec6306e39","tools_ref":"52ab03d8ec104abb85572294bf8a469d75c5de9e74d6042caabff8017adc572b"},"response":{"text":"","thoughts":null,"tool_calls":[{"args":{"answer":"Estimativa ing\u00eanua (infer.ols): coef.rv = -0,3548.","caveats":[],"figure_refs":["call_ee705585:coef.rv"]},"llm_name":"submit_answer","tool_id":null,"vendor_id":"script_2"}]},"response_id":null,"run_id":"20261015T171027Z-suite-crb_papers-naive-54c3f2","seq":3,"task_id":"crb_papers.q108","timestamp":"2026-10-15T17:10:30.417255Z","usage":{"cached":0,"input":0,"output":0,"thoughts":0},"vendor":false}
{"attempt":1,"call_id":"call_53c224b0","epoch":1,"event":"guard","numbers_found":["-0,3548"],"retries_left":1,"run_id":"20261015T171027Z-suite-crb_papers-naive-54c3f2","seq":4,"task_id":"crb_papers.q108","timestamp":"2026-10-15T17:10:30.418596Z","turn_id":"call_fbfb9923","unbacked":[],"uncited":[],"verdict":"accepted"}
{"answer":"Estimativa ing\u00eanua (infer.ols): coef.rv = -0,3548.","answer_figure":{"call_id":"call_ee705585","ci_high":0.07975763136194047,"ci_low":-0.7892676123999026,"formatted":-0.3547549905189811,"key":"coef.rv","ref":"call_ee705585:coef.rv","se":0.22062095776891874,"tool_id":"infer.ols","unit":"","value":-0.3547549905189811},"attempt":1,"caveats":[],"epoch":1,"event":"answer","figure_refs":["call_ee705585:coef.rv"],"run_id":"20261015T171027Z-suite-crb_papers-naive-54c3f2","seq":5,"stopped":"answered","task_id":"crb_papers.q108","timestamp":"2026-10-15T17:10:30.418785Z"}
{"attempt":1,"check":{"check_index":0,"detail":"every number backed","expected":null,"failure_class":null,"kind":"answer_backed","observed":[],"passed":true,"required":true,"source_id":null,"tolerance":null,"trace_call_id":null,"weight":1.0},"epoch":1,"event":"score","run_id":"20261015T171027Z-suite-crb_papers-naive-54c3f2","seq":6,"task_id":"crb_papers.q108","timestamp":"2026-10-15T17:10:30.418785Z"}
{"attempt":1,"check":{"check_index":1,"detail":"-0.3547549905 in [-0.9230968171, 0.07500561537]?","expected":[-0.9230968170952967,0.07500561536668354],"failure_class":null,"kind":"answer_figure_close","observed":-0.3547549905189811,"passed":true,"required":true,"source_id":null,"tolerance":{"abs_tol":null,"interval":[-0.9230968170952967,0.07500561536668354],"min_lre":null,"open_interval":false,"rel_tol":null,"rule":"interval"},"trace_call_id":"call_ee705585","weight":1.0},"epoch":1,"event":"score","run_id":"20261015T171027Z-suite-crb_papers-naive-54c3f2","seq":7,"task_id":"crb_papers.q108","timestamp":"2026-10-15T17:10:30.418785Z"}
{"attempt":1,"check":{"check_index":2,"detail":"-0.3547549905 vs -0.42404560086430654","expected":-0.42404560086430654,"failure_class":null,"kind":"answer_figure_close","observed":-0.3547549905189811,"passed":false,"required":false,"source_id":"crb_papers.q108.gold","tolerance":{"abs_tol":2.546179674647909e-07,"interval":null,"min_lre":null,"open_interval":false,"rel_tol":1e-06,"rule":null},"trace_call_id":"call_ee705585","weight":1.0},"epoch":1,"event":"score","run_id":"20261015T171027Z-suite-crb_papers-naive-54c3f2","seq":8,"task_id":"crb_papers.q108","timestamp":"2026-10-15T17:10:30.418785Z"}
{"attempt":1,"check":{"check_index":3,"detail":"0.2206209578 vs 0.2546179674647909","expected":0.2546179674647909,"failure_class":null,"kind":"answer_figure_close","observed":0.22062095776891874,"passed":false,"required":false,"source_id":"crb_papers.q108.gold","tolerance":{"abs_tol":2.546179674647909e-07,"interval":null,"min_lre":null,"open_interval":false,"rel_tol":1e-06,"rule":null},"trace_call_id":"call_ee705585","weight":1.0},"epoch":1,"event":"score","run_id":"20261015T171027Z-suite-crb_papers-naive-54c3f2","seq":9,"task_id":"crb_papers.q108","timestamp":"2026-10-15T17:10:30.418785Z"}