{"id":"W3111500892","doi":"10.1002/cjs.11685","title":"Better experimental design by hybridizing binary matching with imbalance optimization","year":2022,"lang":"en","type":"preprint","venue":"Canadian Journal of Statistics","topic":"Advanced Causal Inference Techniques","field":"Mathematics","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"United States-Israel Binational Science Foundation","keywords":"Covariate; Estimator; Matching (statistics); Extant taxon; Statistics; Robustness (evolution); Heuristic; Mathematics; Design of experiments; Mean squared error; Word error rate; Greedy algorithm; Binary number; Set (abstract data type); Computer science; Mathematical optimization; Algorithm; Artificial intelligence","routes":{"ca_aff":false,"ca_fund":false,"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":[0.06897121,0.001485778,0.003089952,0.001964659,0.001133954,0.001855222,0.003675557,0.002610553,0.0103388],"category_scores_gemma":[0.135009,0.001409807,0.001979268,0.002405985,0.002830982,0.002607202,0.005273366,0.003031932,0.001952223],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001892815,"about_ca_system_score_gemma":0.003116004,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0006416665,"about_ca_topic_score_gemma":0.0007138124,"domain_scores_codex":[0.9079776,0.07663053,0.002484652,0.006215421,0.005572397,0.001119449],"domain_scores_gemma":[0.9066472,0.05911216,0.008253989,0.01933889,0.004998474,0.001649279],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.008763493,0.002879408,0.008347336,0.001328927,0.0008827881,0.0001694733,0.001112115,0.1135669,0.02480244,0.253381,0.007100901,0.5776652],"study_design_scores_gemma":[0.004588036,0.003979802,0.005534559,0.0001999432,0.0003690595,0.0001539473,0.0001251315,0.4705271,0.01674378,0.4808789,0.01672259,0.0001772003],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.008327113,0.00008178518,0.9892839,0.0003039041,0.00007053187,0.000785897,0.00009745242,0.0004487953,0.0006006125],"genre_scores_gemma":[0.08591064,0.00005591326,0.9092721,0.000475052,0.0000808333,0.002975113,0.0001674788,0.0001750379,0.0008878102],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.06897121,"threshold_uncertainty_score":0.364759,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.09052559107560555,"score_gpt":0.3392478178701193,"score_spread":0.2487222267945137,"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."}}