{"attempt":1,"datasets":{},"epoch":1,"event":"sample_init","question":"","question_sha256":"e3b0c44298fc1c149afbf4c8996fb92427ae41e4649b934ca495991b7852b855","run_id":"20261015T171311Z-suite-dgp-scripted-505dfe","seq":0,"skills":[],"task_id":"dgp.rdd.fast","task_sha256":"deae3479caa076b69963f31a1d0cf5d37d89e570bfb49fd7e66c53ad1cc63934","timestamp":"2026-10-15T17:13:12.703566Z"}
{"arguments":{"bandwidth":null,"cluster":null,"cutoff":0.0,"dataset":"d","kernel":"triangular","level":0.95,"polynomial":1,"running":"margem","subset":null,"y":"reeleito"},"attempt":1,"cached":false,"call_id":"call_79399905","card_version":"0.1.0","code_version":"2925aeab2262","dataset_digests":{"d":"7c7ffeb4a39e105c442f1081fbcd41f5726294c001e68e4e383a51786194099c"},"decided_by":"code","duration_ms":3.8531669415533543,"epoch":1,"error":null,"event":"tool","informed_by":[],"input_digest":"29bd636d07c089f29c27960212dee0b5ea79e34b24d88c5bfeb4908e1889ec32","llm_name":null,"output_digest":"1b0fbe8e1f96ded29e4b6f0497a1bdd86d307f7cc960ad3a5fc16b272afa5194","rationale":"","replay_class":"replayed","result":{"assumptions":["units cannot precisely control the running variable around the cutoff","every other determinant of the outcome is continuous at the cutoff"],"call_id":"call_79399905","datasets":{},"diagnostics":[{"flag":false,"name":"bandwidth","note":"half-width of the window around the cutoff (Imbens\u2013Kalyanaraman)","value":0.9158422423600313},{"flag":false,"name":"bandwidth_method","note":"","value":"IK"},{"flag":false,"name":"n_left","note":"observations with positive weight below the cutoff","value":1845},{"flag":false,"name":"n_right","note":"observations with positive weight at or above the cutoff","value":1841},{"flag":false,"name":"kernel","note":"","value":"triangular"},{"flag":false,"name":"polynomial","note":"","value":1},{"flag":false,"name":"bandwidth_sensitive","note":"the estimates at half and double the bandwidth fall inside the main interval","value":false},{"flag":false,"name":"n_obs","note":"rows used to choose the bandwidth","value":4000},{"flag":false,"name":"se_type","note":"heteroskedasticity-robust standard errors (HC1)","value":"HC1"}],"figures":[{"ci_high":0.25832899461416453,"ci_level":0.95,"ci_low":0.1737332067644808,"derivation":{"note_pt":"","steps":[{"formula":"","result":0.21603110068932266,"terms":[{"note":"janela (Imbens\u2013Kalyanaraman)","symbol":"h","unit":"","value":0.9158422423600313},{"note":"","symbol":"n_esq","unit":"observa\u00e7\u00f5es abaixo do limiar","value":1845.0},{"note":"","symbol":"n_dir","unit":"observa\u00e7\u00f5es acima do limiar","value":1841.0}],"text_pt":"Ajuste linear ponderado (kernel triangular) de cada lado do limiar, com janela h (Imbens\u2013Kalyanaraman); o efeito \u00e9 a diferen\u00e7a entre os dois ajustes no limiar.","unit":""},{"formula":"ep = \u221aV_jj","result":0.02158095468002588,"terms":[{"note":"diagonal da matriz de covari\u00e2ncia","symbol":"V_jj","unit":"","value":0.0004657376049013309}],"text_pt":"Erro-padr\u00e3o robusto \u00e0 heterocedasticidade (HC1): a raiz da vari\u00e2ncia estimada do coeficiente.","unit":""},{"formula":"IC = \u03c4 \u00b1 c \u00b7 ep","result":null,"terms":[{"note":"","symbol":"\u03c4","unit":"","value":0.21603110068932266},{"note":"quantil 0.975","symbol":"c","unit":"","value":1.959963984540054},{"note":"","symbol":"ep","unit":"","value":0.02158095468002588}],"text_pt":"Intervalo de 95% de confian\u00e7a: a estimativa mais ou menos o valor cr\u00edtico da distribui\u00e7\u00e3o normal vezes o erro-padr\u00e3o.","unit":""}]},"key":"effect","label":"Jump in reeleito at the cutoff of margem","se":0.02158095468002588,"unit":"","value":0.21603110068932266}],"notes":"","reason":"","status":"ok","tables":{"sensitivity":[{"bandwidth":0.45792112118001566,"ci_high":0.27560884113994305,"ci_low":0.1579184426881498,"estimate":0.21676364191404643,"multiplier":0.5,"n_left":904,"n_right":916,"se":0.03002361252046468},{"bandwidth":0.9158422423600313,"ci_high":0.25832899461416453,"ci_low":0.1737332067644808,"estimate":0.21603110068932266,"multiplier":1.0,"n_left":1845,"n_right":1841,"se":0.02158095468002588},{"bandwidth":1.8316844847200626,"ci_high":0.25620020490005796,"ci_low":0.1807044910108871,"estimate":0.21845234795547253,"multiplier":2.0,"n_left":2004,"n_right":1996,"se":0.019259464583194247}]},"tool":"infer.rdd","version":"0.1.0","warnings":[]},"result_for_model_ref":null,"result_sha256":"6c4cf570dc8d74e1d393af2da5cdf1632f20a3876d92acf76297f153d8c82d5b","run_id":"20261015T171311Z-suite-dgp-scripted-505dfe","seq":1,"synthetic":false,"task_id":"dgp.rdd.fast","timestamp":"2026-10-15T17:13:12.703566Z","tool_id":"infer.rdd","turn_id":null}
{"arguments":{"bandwidth":null,"cluster":null,"cutoff":0.0,"dataset":"d","kernel":"triangular","level":0.95,"polynomial":1,"running":"margem","subset":null,"y":"reeleito"},"attempt":1,"cached":false,"call_id":"call_c1250dc1","card_version":"0.1.0","code_version":"2925aeab2262","dataset_digests":{"d":"ec721e5ac6574b03ff6b6fbc51a4b8198cc5fab4f221be8a568bff92548a5a71"},"decided_by":"code","duration_ms":2.79699987731874,"epoch":1,"error":null,"event":"tool","informed_by":[],"input_digest":"29bd636d07c089f29c27960212dee0b5ea79e34b24d88c5bfeb4908e1889ec32","llm_name":null,"output_digest":"aca298875b584620908633933e561043b1473e3f8638d402b0b834c050621d9d","rationale":"","replay_class":"replayed","result":{"assumptions":["units cannot precisely control the running variable around the cutoff","every other determinant of the outcome is continuous at the cutoff"],"call_id":"call_c1250dc1","datasets":{},"diagnostics":[{"flag":false,"name":"bandwidth","note":"half-width of the window around the cutoff (Imbens\u2013Kalyanaraman)","value":0.6274121798752506},{"flag":false,"name":"bandwidth_method","note":"","value":"IK"},{"flag":false,"name":"n_left","note":"observations with positive weight below the cutoff","value":1274},{"flag":false,"name":"n_right","note":"observations with positive weight at or above the cutoff","value":1250},{"flag":false,"name":"kernel","note":"","value":"triangular"},{"flag":false,"name":"polynomial","note":"","value":1},{"flag":false,"name":"bandwidth_sensitive","note":"the estimates at half and double the bandwidth fall inside the main interval","value":false},{"flag":false,"name":"n_obs","note":"rows used to choose the bandwidth","value":4000},{"flag":false,"name":"se_type","note":"heteroskedasticity-robust standard errors (HC1)","value":"HC1"}],"figures":[{"ci_high":0.2474854887453401,"ci_level":0.95,"ci_low":0.14413796994959654,"derivation":{"note_pt":"","steps":[{"formula":"","result":0.19581172934746832,"terms":[{"note":"janela (Imbens\u2013Kalyanaraman)","symbol":"h","unit":"","value":0.6274121798752506},{"note":"","symbol":"n_esq","unit":"observa\u00e7\u00f5es abaixo do limiar","value":1274.0},{"note":"","symbol":"n_dir","unit":"observa\u00e7\u00f5es acima do limiar","value":1250.0}],"text_pt":"Ajuste linear ponderado (kernel triangular) de cada lado do limiar, com janela h (Imbens\u2013Kalyanaraman); o efeito \u00e9 a diferen\u00e7a entre os dois ajustes no limiar.","unit":""},{"formula":"ep = \u221aV_jj","result":0.026364647414680992,"terms":[{"note":"diagonal da matriz de covari\u00e2ncia","symbol":"V_jj","unit":"","value":0.0006950946333004451}],"text_pt":"Erro-padr\u00e3o robusto \u00e0 heterocedasticidade (HC1): a raiz da vari\u00e2ncia estimada do coeficiente.","unit":""},{"formula":"IC = \u03c4 \u00b1 c \u00b7 ep","result":null,"terms":[{"note":"","symbol":"\u03c4","unit":"","value":0.19581172934746832},{"note":"quantil 0.975","symbol":"c","unit":"","value":1.959963984540054},{"note":"","symbol":"ep","unit":"","value":0.026364647414680992}],"text_pt":"Intervalo de 95% de confian\u00e7a: a estimativa mais ou menos o valor cr\u00edtico da distribui\u00e7\u00e3o normal vezes o erro-padr\u00e3o.","unit":""}]},"key":"effect","label":"Jump in reeleito at the cutoff of margem","se":0.026364647414680992,"unit":"","value":0.19581172934746832}],"notes":"","reason":"","status":"ok","tables":{"sensitivity":[{"bandwidth":0.3137060899376253,"ci_high":0.26340515296204836,"ci_low":0.11948827843433739,"estimate":0.19144671569819288,"multiplier":0.5,"n_left":653,"n_right":676,"se":0.03671416303128755},{"bandwidth":0.6274121798752506,"ci_high":0.2474854887453401,"ci_low":0.14413796994959654,"estimate":0.19581172934746832,"multiplier":1.0,"n_left":1274,"n_right":1250,"se":0.026364647414680992},{"bandwidth":1.254824359750501,"ci_high":0.2432142688604543,"ci_low":0.16477600253068156,"estimate":0.20399513569556793,"multiplier":2.0,"n_left":2040,"n_right":1960,"se":0.02001012950964502}]},"tool":"infer.rdd","version":"0.1.0","warnings":[]},"result_for_model_ref":null,"result_sha256":"cc47ceb87f5f3eee0f81f3fcbdc5be6562f2dae73ec90954e662166deb796a93","run_id":"20261015T171311Z-suite-dgp-scripted-505dfe","seq":2,"synthetic":false,"task_id":"dgp.rdd.fast","timestamp":"2026-10-15T17:13:12.708067Z","tool_id":"infer.rdd","turn_id":null}
{"arguments":{"bandwidth":null,"cluster":null,"cutoff":0.0,"dataset":"d","kernel":"triangular","level":0.95,"polynomial":1,"running":"margem","subset":null,"y":"reeleito"},"attempt":1,"cached":false,"call_id":"call_f81b76ef","card_version":"0.1.0","code_version":"2925aeab2262","dataset_digests":{"d":"c7ecace56f5ba906474a39051b8a7356e0e83ae52fd5b741d4d625c8fdba656f"},"decided_by":"code","duration_ms":2.7000419795513153,"epoch":1,"error":null,"event":"tool","informed_by":[],"input_digest":"29bd636d07c089f29c27960212dee0b5ea79e34b24d88c5bfeb4908e1889ec32","llm_name":null,"output_digest":"a6106c0b716d5c9fc42bcf20b66e04a3ffefeae07641a19a8030d78e3c3858d7","rationale":"","replay_class":"replayed","result":{"assumptions":["units cannot precisely control the running variable around the cutoff","every other determinant of the outcome is continuous at the cutoff"],"call_id":"call_f81b76ef","datasets":{},"diagnostics":[{"flag":false,"name":"bandwidth","note":"half-width of the window around the cutoff (Imbens\u2013Kalyanaraman)","value":0.6024806523064873},{"flag":false,"name":"bandwidth_method","note":"","value":"IK"},{"flag":false,"name":"n_left","note":"observations with positive weight below the cutoff","value":1225},{"flag":false,"name":"n_right","note":"observations with positive weight at or above the cutoff","value":1211},{"flag":false,"name":"kernel","note":"","value":"triangular"},{"flag":false,"name":"polynomial","note":"","value":1},{"flag":false,"name":"bandwidth_sensitive","note":"the estimates at half and double the bandwidth fall inside the main interval","value":false},{"flag":false,"name":"n_obs","note":"rows used to choose the bandwidth","value":4000},{"flag":false,"name":"se_type","note":"heteroskedasticity-robust standard errors (HC1)","value":"HC1"}],"figures":[{"ci_high":0.29065355341656773,"ci_level":0.95,"ci_low":0.1841495208292407,"derivation":{"note_pt":"","steps":[{"formula":"","result":0.2374015371229042,"terms":[{"note":"janela (Imbens\u2013Kalyanaraman)","symbol":"h","unit":"","value":0.6024806523064873},{"note":"","symbol":"n_esq","unit":"observa\u00e7\u00f5es abaixo do limiar","value":1225.0},{"note":"","symbol":"n_dir","unit":"observa\u00e7\u00f5es acima do limiar","value":1211.0}],"text_pt":"Ajuste linear ponderado (kernel triangular) de cada lado do limiar, com janela h (Imbens\u2013Kalyanaraman); o efeito \u00e9 a diferen\u00e7a entre os dois ajustes no limiar.","unit":""},{"formula":"ep = \u221aV_jj","result":0.02716989532139806,"terms":[{"note":"diagonal da matriz de covari\u00e2ncia","symbol":"V_jj","unit":"","value":0.0007382032117757282}],"text_pt":"Erro-padr\u00e3o robusto \u00e0 heterocedasticidade (HC1): a raiz da vari\u00e2ncia estimada do coeficiente.","unit":""},{"formula":"IC = \u03c4 \u00b1 c \u00b7 ep","result":null,"terms":[{"note":"","symbol":"\u03c4","unit":"","value":0.2374015371229042},{"note":"quantil 0.975","symbol":"c","unit":"","value":1.959963984540054},{"note":"","symbol":"ep","unit":"","value":0.02716989532139806}],"text_pt":"Intervalo de 95% de confian\u00e7a: a estimativa mais ou menos o valor cr\u00edtico da distribui\u00e7\u00e3o normal vezes o erro-padr\u00e3o.","unit":""}]},"key":"effect","label":"Jump in reeleito at the cutoff of margem","se":0.02716989532139806,"unit":"","value":0.2374015371229042}],"notes":"","reason":"","status":"ok","tables":{"sensitivity":[{"bandwidth":0.30124032615324364,"ci_high":0.3178174875042531,"ci_low":0.17002548893905514,"estimate":0.2439214882216541,"multiplier":0.5,"n_left":598,"n_right":620,"se":0.03770273324687656},{"bandwidth":0.6024806523064873,"ci_high":0.29065355341656773,"ci_low":0.1841495208292407,"estimate":0.2374015371229042,"multiplier":1.0,"n_left":1225,"n_right":1211,"se":0.02716989532139806},{"bandwidth":1.2049613046129746,"ci_high":0.2837967653882813,"ci_low":0.20579592373793276,"estimate":0.24479634456310703,"multiplier":2.0,"n_left":2020,"n_right":1980,"se":0.019898539530728433}]},"tool":"infer.rdd","version":"0.1.0","warnings":[]},"result_for_model_ref":null,"result_sha256":"10f871be8a1f5b017db9c8ffe69e72631f00b7d957a7b2788fd152ba816483df","run_id":"20261015T171311Z-suite-dgp-scripted-505dfe","seq":3,"synthetic":false,"task_id":"dgp.rdd.fast","timestamp":"2026-10-15T17:13:12.711492Z","tool_id":"infer.rdd","turn_id":null}
{"attempt":1,"call_id":"call_784b1e96","data":{"inputs":{"contexts":100,"kept":3},"outputs":{"outputs_sha256":"2d4e3fc4bc73858fd74aecdaa2b661ca926ab14464e7f0aed5f9215717e69e2e","records":100}},"decided_by":"code","epoch":1,"event":"note","name":"bench.capture","run_id":"20261015T171311Z-suite-dgp-scripted-505dfe","seq":4,"task_id":"dgp.rdd.fast","timestamp":"2026-10-15T17:13:13.030430Z"}
{"attempt":1,"check":{"check_index":0,"detail":"100/100 calls ok","expected":100,"failure_class":null,"kind":"status","observed":100,"passed":true,"required":true,"source_id":null,"tolerance":null,"trace_call_id":null,"weight":1.0},"epoch":1,"event":"score","run_id":"20261015T171311Z-suite-dgp-scripted-505dfe","seq":5,"task_id":"dgp.rdd.fast","timestamp":"2026-10-15T17:13:13.030342Z"}
{"attempt":1,"check":{"check_index":1,"detail":"bias: mean 0.24596 vs truth 0.25, bias -0.0040385 (MC SE 0.00312, R=100); |bias| <= 0.02?","expected":{"tolerance":0.02,"truth":0.25},"failure_class":null,"kind":"figure_close","observed":-0.00403854,"passed":true,"required":true,"source_id":null,"tolerance":null,"trace_call_id":null,"weight":1.0},"epoch":1,"event":"score","run_id":"20261015T171311Z-suite-dgp-scripted-505dfe","seq":6,"task_id":"dgp.rdd.fast","timestamp":"2026-10-15T17:13:13.030342Z"}
{"attempt":1,"check":{"check_index":2,"detail":"coverage of the 95% interval: 0.8700 in [0.8200, 0.9900]? (MC SE 0.0336, R=100) \u2014 conventional intervals at the MSE-optimal bandwidth under-cover (smoothing bias; Calonico, Cattaneo & Titiunik 2014): about 0.92 here with R = 1,000, so the band catches broken standard errors, not that bias","expected":[0.82,0.99],"failure_class":null,"kind":"figure_close","observed":0.87,"passed":true,"required":true,"source_id":null,"tolerance":null,"trace_call_id":null,"weight":1.0},"epoch":1,"event":"score","run_id":"20261015T171311Z-suite-dgp-scripted-505dfe","seq":7,"task_id":"dgp.rdd.fast","timestamp":"2026-10-15T17:13:13.030342Z"}