{"id":"W2588316907","doi":"10.1093/aje/163.suppl_11.s222-c","title":"Bayesian Propensity Score Analysis for Observational Data","year":2006,"lang":"en","type":"article","venue":"American Journal of Epidemiology","topic":"Advanced Causal Inference Techniques","field":"Mathematics","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"","keywords":"Propensity score matching; Observational study; Confidence interval; Confounding; Statistics; Medicine; Bayesian probability; Markov chain Monte Carlo; Credible interval; Odds ratio; Outcome (game theory); Econometrics; Mathematics","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"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":[0.02861687,0.001115036,0.002835408,0.003800315,0.0008151543,0.002697908,0.002620105,0.002442252,0.004781985],"category_scores_gemma":[0.1379598,0.001380542,0.001914187,0.004382401,0.001961469,0.003108265,0.002702767,0.00460352,0.001043842],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001568792,"about_ca_system_score_gemma":0.004175046,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004713761,"about_ca_topic_score_gemma":0.004514261,"domain_scores_codex":[0.9797742,0.01633456,0.0005659284,0.0009091417,0.002124495,0.0002917648],"domain_scores_gemma":[0.9472319,0.04337688,0.002650351,0.003741285,0.002494409,0.00050519],"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":[0.0002501215,0.0001295094,0.005707289,0.0005216713,0.0009661414,0.0001921313,0.0002746644,0.0638102,0.0003943146,0.6431359,0.02808425,0.2565338],"study_design_scores_gemma":[0.0001765734,0.00004130134,0.001392256,0.00009685229,0.0001716172,0.0001532,0.00002328455,0.2433839,0.0001647419,0.7469321,0.007426514,0.00003757346],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.001205816,0.001523672,0.9954015,0.0009889849,0.00007862154,0.00005860676,0.00009119423,0.0001820879,0.000469513],"genre_scores_gemma":[0.1530138,0.006056175,0.8317234,0.0008482913,0.0007284685,0.001286014,0.001024792,0.0003114989,0.005007636],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.02861687,"threshold_uncertainty_score":0.1513423,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.5585941002671695,"score_gpt":0.4887832849630621,"score_spread":0.06981081530410738,"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."}}