{"id":"W2324021236","doi":"10.1097/ede.0000000000000237","title":"Variable Selection for Propensity Score Estimation via Balancing Covariates","year":2015,"lang":"en","type":"letter","venue":"Epidemiology","topic":"Advanced Causal Inference Techniques","field":"Mathematics","cited_by":38,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"","keywords":"Covariate; Propensity score matching; Statistics; Confounding; Average treatment effect; Estimator; Outcome (game theory); Matching (statistics); Causal inference; Inverse probability weighting; Poisson distribution; Econometrics; Mathematics","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":true,"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":[0.02542902,0.001060192,0.001497889,0.001118861,0.0009665991,0.003179251,0.003479123,0.005359624,0.02182295],"category_scores_gemma":[0.1765558,0.0006596932,0.00131955,0.001876057,0.002346339,0.002367119,0.001540707,0.01075788,0.005929821],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001029721,"about_ca_system_score_gemma":0.002635974,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001400373,"about_ca_topic_score_gemma":0.002042806,"domain_scores_codex":[0.9885104,0.007911175,0.0007989363,0.0009113286,0.001635251,0.0002328239],"domain_scores_gemma":[0.9041687,0.0745644,0.003847491,0.004073742,0.01178284,0.001562782],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.0002857927,0.00005386057,0.002106972,0.0006210192,0.0002906877,0.0003891938,0.0001339623,0.001905463,0.0001109102,0.01154955,0.8634273,0.1191253],"study_design_scores_gemma":[0.0005796836,0.0002400689,0.002978424,0.001552256,0.0004976079,0.00155672,0.000117403,0.0421396,0.0008300378,0.07756368,0.8717836,0.0001608684],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"commentary","genre_gemma":"methods","genre_scores_codex":[0.002910694,0.01512597,0.1716337,0.4644103,0.3391707,0.0002604351,0.0009782634,0.00121425,0.004295791],"genre_scores_gemma":[0.05681478,0.01658182,0.08596,0.1826664,0.635294,0.0007598865,0.0007601837,0.001242505,0.01992031],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.02542902,"threshold_uncertainty_score":0.1344832,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.3584312581013067,"score_gpt":0.436247978124035,"score_spread":0.07781672002272838,"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."}}