{"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":"codex-gemma-dda1882f352a","candidate_categories":["metaresearch","metaepi_narrow","research_integrity"],"consensus_categories":[],"category_scores_codex":[0.004506626,0.000441062,0.001448986,0.0001624132,0.0001376374,0.00001381714,0.0002891069,0.001720705,0.00007143147],"category_scores_gemma":[0.02953544,0.0003888001,0.0001224919,0.0001694969,0.0001059183,0.000192519,0.000100623,0.001480632,0.00003326355],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004791608,"about_ca_system_score_gemma":0.0002283829,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001923061,"about_ca_topic_score_gemma":0.00002320516,"domain_scores_codex":[0.996722,0.0007955206,0.0009505282,0.0006706942,0.0001447658,0.0007164693],"domain_scores_gemma":[0.9894828,0.008326659,0.001027122,0.0004954843,0.0006017964,0.00006613835],"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":[0.00002316355,0.00001532043,0.0003544269,0.000430991,0.00005720898,0.000003058539,0.00002306911,0.000178785,0.0001370114,0.06527231,0.9327915,0.000713123],"study_design_scores_gemma":[0.0001526391,0.0002057985,0.00001583367,0.0001462852,0.0001071427,0.00006390759,9.590617e-7,0.01926219,0.0002286141,0.8566715,0.1228057,0.0003393864],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.00005098451,0.00005073023,0.9242165,0.07246182,0.0004699406,0.001438543,0.00003981352,0.0007277791,0.0005438622],"genre_scores_gemma":[0.0001361219,0.000004334735,0.8523003,0.1431154,0.001932633,0.0005615551,0.0007384787,0.00009838311,0.00111273],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.8099858,"threshold_uncertainty_score":0.9998564,"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."}}