{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007261311,0.0001333341,0.0004612968,0.0001862335,0.00008170024,0.000003750986,0.0003750316,0.00005485578,0.0001667403],"category_scores_gemma":[0.006512995,0.0001089723,0.00001774672,0.000521474,0.000247228,0.000115028,0.0001078845,0.0001549737,0.000001345157],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00006198909,"about_ca_system_score_gemma":0.00008304309,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00007261337,"about_ca_topic_score_gemma":0.0005618366,"domain_scores_codex":[0.9986523,0.0000419492,0.0004597841,0.0003118837,0.0003330228,0.000201013],"domain_scores_gemma":[0.9973747,0.001426141,0.0001632981,0.0007299104,0.000244806,0.00006113388],"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":[0.0000753707,0.000150832,0.224639,0.0002017388,0.000235417,0.0001035622,0.0006362728,0.00005490006,0.0002096894,0.680292,0.09114838,0.002252774],"study_design_scores_gemma":[0.0005780605,0.0001432014,0.06224351,0.00007867767,0.0002734842,0.000008611961,0.00003949451,0.02177053,0.00008835609,0.9137309,0.0008612667,0.0001838765],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.007175769,0.00002527949,0.9907711,0.0003653258,0.00005177388,0.0004567262,0.0007191345,0.00007583357,0.0003590422],"genre_scores_gemma":[0.1781546,0.0000574043,0.8194624,0.0002216314,0.0001087414,0.00004370886,0.001603944,0.00001848757,0.0003290916],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.2334389,"threshold_uncertainty_score":0.7797134,"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."}}