{"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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0006562773,0.0005172758,0.001632916,0.00009745953,0.0001935814,0.00001186378,0.0003147382,0.001237451,0.00007583234],"category_scores_gemma":[0.003871852,0.0003745465,0.0001336265,0.00002425844,0.0004556855,0.0001045678,0.0001812663,0.001051859,0.00003110375],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003119999,"about_ca_system_score_gemma":0.00008556622,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00007676673,"about_ca_topic_score_gemma":0.00001564871,"domain_scores_codex":[0.997241,0.0005882774,0.0006807974,0.0007836433,0.0001099153,0.0005963451],"domain_scores_gemma":[0.9950759,0.002567655,0.0008565296,0.001309315,0.0001062704,0.0000843001],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00001068836,0.00002870618,0.006212024,0.000138417,0.0001136381,0.0001300396,0.00003255606,2.874941e-7,0.000005914406,0.005951621,0.9844074,0.002968708],"study_design_scores_gemma":[0.0002346226,0.0004652985,0.00260614,0.0001416036,0.000130752,0.0002025266,0.000001730729,0.00004013144,0.00003453473,0.3715479,0.6243186,0.0002761619],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"commentary","genre_gemma":"commentary","genre_scores_codex":[0.002838887,0.003139718,0.0232969,0.9465792,0.00202113,0.004165393,0.0006370756,0.001704366,0.01561736],"genre_scores_gemma":[0.002686304,0.01082532,0.1489027,0.758316,0.02183788,0.003222112,0.00181103,0.0004422435,0.05195635],"genre_candidate":"commentary","genre_consensus":"commentary","teacher_disagreement_score":0.3655963,"threshold_uncertainty_score":0.9998707,"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."}}