{"id":"W4382987679","doi":"10.1002/cjs.11783","title":"Rerandomization and optimal matching","year":2023,"lang":"en","type":"article","venue":"Canadian Journal of Statistics","topic":"Advanced Causal Inference Techniques","field":"Mathematics","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"U.S. Food and Drug Administration; University of Waterloo","keywords":"Covariate; Randomization; Mean squared error; Robustness (evolution); Restricted randomization; Matching (statistics); Statistics; Balance (ability); Computer science; Mathematics; Factorial experiment; Measure (data warehouse); Mathematical optimization; Randomized controlled trial; Data mining; Medicine","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.02753136,0.0007989476,0.002520929,0.00124456,0.0006880682,0.0009251636,0.002562384,0.001960527,0.008763769],"category_scores_gemma":[0.06915122,0.0007871902,0.001122267,0.001382684,0.002389329,0.001608937,0.002735015,0.002128561,0.001250426],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001427471,"about_ca_system_score_gemma":0.002011633,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0005270818,"about_ca_topic_score_gemma":0.0004279726,"domain_scores_codex":[0.9395458,0.05105205,0.001631811,0.003941128,0.002858626,0.0009705971],"domain_scores_gemma":[0.9608777,0.01967576,0.00498022,0.01130495,0.002376294,0.0007851718],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.007393904,0.001253687,0.003891202,0.0008188533,0.0004876841,0.0001629427,0.000441075,0.1162577,0.008391091,0.4195877,0.006031213,0.4352829],"study_design_scores_gemma":[0.003760068,0.004154038,0.004161606,0.0003436283,0.0003057891,0.0003310686,0.0001416819,0.4218455,0.01055051,0.5385755,0.01570021,0.0001304049],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.0234422,0.000232892,0.971583,0.0004764976,0.00008381208,0.001161156,0.00009590665,0.0002557627,0.002668775],"genre_scores_gemma":[0.404897,0.0001798939,0.5865647,0.0008204467,0.00009782535,0.003680883,0.0002045354,0.0001071081,0.003447591],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.02753136,"threshold_uncertainty_score":0.1456015,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.09737256076111298,"score_gpt":0.3583080507829147,"score_spread":0.2609354900218017,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}