{"id":"W2602905072","doi":"10.1097/ede.0000000000000657","title":"Treatment Prediction, Balance, and Propensity Score Adjustment","year":2017,"lang":"en","type":"letter","venue":"Epidemiology","topic":"Advanced Causal Inference Techniques","field":"Mathematics","cited_by":11,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University","funders":"","keywords":"Propensity score matching; Covariate; Confounding; Causal inference; Statistics; Logistic regression; Econometrics; National Health and Nutrition Examination Survey; Medicine; Inverse probability weighting; Average treatment effect; Weighting; Mathematics; Population; Environmental health","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":[0.04579686,0.001005726,0.001796478,0.001612395,0.001213404,0.003494252,0.004500819,0.00893317,0.008498797],"category_scores_gemma":[0.3002971,0.0007475733,0.00118216,0.001794689,0.003539552,0.002991175,0.001365964,0.01549403,0.002696693],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001785912,"about_ca_system_score_gemma":0.003608078,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002588468,"about_ca_topic_score_gemma":0.003603178,"domain_scores_codex":[0.9813476,0.01147448,0.001991471,0.001161265,0.003598916,0.0004262968],"domain_scores_gemma":[0.8330268,0.1256036,0.007850438,0.005420432,0.02566814,0.00243071],"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":[0.0002440951,0.00004284671,0.001300501,0.0004226933,0.0001742052,0.0001982788,0.00007147758,0.0003386941,0.00003286795,0.003903638,0.9526125,0.04065834],"study_design_scores_gemma":[0.0006865939,0.000294352,0.009079694,0.003511338,0.0007451109,0.001058485,0.0002017916,0.009656476,0.0006998234,0.03625979,0.937592,0.0002145344],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"commentary","genre_gemma":"methods","genre_scores_codex":[0.001068359,0.01224419,0.004377348,0.6674803,0.3125917,0.00008065812,0.0003598268,0.0001133472,0.001684227],"genre_scores_gemma":[0.02385718,0.01224549,0.0052853,0.2628057,0.6892425,0.0001992191,0.0002488373,0.0001989892,0.005916831],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.04579686,"threshold_uncertainty_score":0.2421998,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.3952037677165637,"score_gpt":0.4328986115020504,"score_spread":0.03769484378548671,"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."}}