{"id":"W2906741671","doi":"10.1002/sim.8075","title":"Should a propensity score model be super? The utility of ensemble procedures for causal adjustment","year":2018,"lang":"en","type":"article","venue":"Statistics in Medicine","topic":"Advanced Causal Inference Techniques","field":"Mathematics","cited_by":35,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University Health Centre; McGill University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Propensity score matching; Covariate; Logistic regression; Statistics; Inverse probability weighting; Overfitting; Weighting; Regression; Matching (statistics); Mean squared error; Econometrics; Mathematics; Confounding; Computer science; Artificial intelligence; Medicine","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009891925,0.000186805,0.0004472336,0.00006447617,0.00007758097,0.000003991475,0.0002538093,0.00008410043,0.00008509209],"category_scores_gemma":[0.005688027,0.0001136141,0.00002209875,0.0001450924,0.0008366736,0.00005080394,0.0000892793,0.0002079182,6.117556e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005817179,"about_ca_system_score_gemma":0.0001856657,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00008929595,"about_ca_topic_score_gemma":0.001479168,"domain_scores_codex":[0.9984712,0.00005564007,0.0005338228,0.0002578858,0.0003939316,0.0002875013],"domain_scores_gemma":[0.9977834,0.0009606837,0.0001802252,0.0004743617,0.0005441268,0.00005726732],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.0003290676,0.0002767449,0.001247534,0.001162089,0.00004399951,0.000006481744,0.005485426,0.00001107937,0.006251137,0.8530927,0.1263668,0.00572694],"study_design_scores_gemma":[0.0005562737,0.0007355502,0.0009763699,0.0003016611,0.00007748333,0.000005235939,0.0003506729,0.03221045,0.01137419,0.9531544,0.0001236116,0.0001340832],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.03792845,0.00006654701,0.9578023,0.000717934,0.0001009733,0.001639429,0.0002672915,0.00007212951,0.001404954],"genre_scores_gemma":[0.7115288,0.00004038946,0.2876122,0.0003320292,0.0001139116,0.0001493165,0.00002250929,0.0000232889,0.0001775066],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.6736004,"threshold_uncertainty_score":0.680951,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.352053424871457,"score_gpt":0.4614552599099146,"score_spread":0.1094018350384576,"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."}}