{"id":"W2063846874","doi":"10.1002/sim.3460","title":"Bayesian propensity score analysis for observational data","year":2008,"lang":"en","type":"article","venue":"Statistics in Medicine","topic":"Advanced Causal Inference Techniques","field":"Mathematics","cited_by":117,"is_retracted":false,"has_abstract":true,"ca_institutions":"Institute for Clinical Evaluative Sciences; University of Toronto; University of British Columbia; Simon Fraser University","funders":"","keywords":"Propensity score matching; Observational study; Statistics; Confounding; Confidence interval; Bayesian probability; Markov chain Monte Carlo; Credible interval; Econometrics; Odds ratio; Outcome (game theory); Medicine; 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":[0.06296529,0.001467818,0.00324572,0.00621932,0.001218935,0.002885757,0.003214149,0.002833264,0.004752407],"category_scores_gemma":[0.2271322,0.001406983,0.002420949,0.006348886,0.003241325,0.004603465,0.003950641,0.004547124,0.001159238],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002432831,"about_ca_system_score_gemma":0.00484258,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005453109,"about_ca_topic_score_gemma":0.002953466,"domain_scores_codex":[0.9512708,0.04054193,0.00151696,0.00248321,0.003717237,0.0004698537],"domain_scores_gemma":[0.851211,0.1289234,0.007827906,0.007819886,0.003556833,0.0006609784],"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":[0.0002716239,0.0001198787,0.01142528,0.0008230133,0.001162454,0.0003174461,0.0004928259,0.1894581,0.0005250712,0.5808557,0.005910179,0.2086385],"study_design_scores_gemma":[0.0001626168,0.0000731428,0.002012622,0.0001563307,0.0001209511,0.0001331978,0.00004052274,0.4282334,0.0002764224,0.5637193,0.005013026,0.00005853629],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.001520006,0.0004351237,0.9970471,0.0002928573,0.00002709568,0.0001184481,0.0001189729,0.0001701135,0.0002702246],"genre_scores_gemma":[0.1478337,0.002442408,0.8440492,0.0004994221,0.0003613586,0.002035586,0.001267691,0.0002161039,0.001294487],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.06296529,"threshold_uncertainty_score":0.3329962,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.5909869107336676,"score_gpt":0.490360892353943,"score_spread":0.1006260183797246,"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."}}