{"id":"W2736383080","doi":"10.1177/0962280217715487","title":"A model averaging approach for estimating propensity scores by optimizing balance","year":2017,"lang":"en","type":"article","venue":"Statistical Methods in Medical Research","topic":"Advanced Causal Inference Techniques","field":"Mathematics","cited_by":22,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"","keywords":"Propensity score matching; Covariate; Estimator; Statistics; Statistic; Nonparametric statistics; Inverse probability weighting; Causal inference; Weighting; Mathematics; Parametric statistics; Econometrics; Parametric model; Computer science; Medicine","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.01160382,0.001295149,0.002067175,0.001953918,0.0006867733,0.001326863,0.001843021,0.001250354,0.003014408],"category_scores_gemma":[0.0228576,0.0008166646,0.002356519,0.00283408,0.0009520077,0.00215489,0.002037112,0.001935811,0.0005143297],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001001438,"about_ca_system_score_gemma":0.002278928,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003321951,"about_ca_topic_score_gemma":0.00249857,"domain_scores_codex":[0.9928318,0.004842576,0.0003215679,0.0008539719,0.0009509057,0.0001991196],"domain_scores_gemma":[0.9950371,0.003164885,0.0005245389,0.000630468,0.000546878,0.00009613337],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.0001232347,0.0001100564,0.003547607,0.0002643085,0.0007637131,0.0001565795,0.0002555373,0.5420759,0.002091783,0.1694623,0.003448882,0.2777],"study_design_scores_gemma":[0.00004225546,0.0001534133,0.0009555232,0.00003629443,0.0001331455,0.0001074963,0.00002168755,0.9065355,0.0008915012,0.08679685,0.004287022,0.00003937936],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.001329835,0.0001149278,0.9980987,0.0000758944,0.00001428371,0.00002267702,0.00002546927,0.00008269005,0.0002355634],"genre_scores_gemma":[0.1440094,0.0007843263,0.8516718,0.0002499083,0.0001876844,0.000612085,0.0003825597,0.0001637696,0.001938375],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.01160382,"threshold_uncertainty_score":0.06136757,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.6341758442076512,"score_gpt":0.6615234680562492,"score_spread":0.02734762384859801,"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."}}