{"id":"W1683369961","doi":"10.1111/biom.12269","title":"On Bayesian Estimation of Marginal Structural Models","year":2015,"lang":"en","type":"article","venue":"Biometrics","topic":"Advanced Causal Inference Techniques","field":"Mathematics","cited_by":56,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University; University of Toronto","funders":"Fonds de Recherche du Québec - Santé; Natural Sciences and Engineering Research Council of Canada; Canadian Institutes of Health Research","keywords":"Censoring (clinical trials); Marginal structural model; Inverse probability; Bayesian probability; Weighting; Bayesian inference; Posterior probability; Inverse probability weighting; Statistics; Inference; Covariate; Marginal likelihood; Population; Econometrics; Computer science; Marginal distribution; Mathematics; Confounding; Estimator; Artificial intelligence; Medicine; Random variable","routes":{"ca_aff":true,"ca_fund":true,"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":[0.0002831212,0.0001058223,0.0001736189,0.0008222827,0.00001805695,0.00001260046,0.0001509285,0.00008104888,0.00001639434],"category_scores_gemma":[0.001511185,0.00009093527,0.00003307998,0.0015514,0.00004551425,0.0001868189,0.00003879919,0.00007821248,0.000005725865],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001132441,"about_ca_system_score_gemma":0.00003878747,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000009540743,"about_ca_topic_score_gemma":6.112447e-7,"domain_scores_codex":[0.9990882,0.00002475705,0.0002340505,0.0001250099,0.0003814774,0.0001465398],"domain_scores_gemma":[0.9990459,0.0002849761,0.0001595061,0.0002562607,0.0001706134,0.00008275679],"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":[0.00003517813,0.00005890712,0.00007441694,0.00006072319,0.00001112333,0.000003171366,0.0001690275,0.001804287,0.0002749835,0.9651593,0.002275741,0.03007313],"study_design_scores_gemma":[0.0001644916,0.000216074,0.0000485246,0.00001992589,0.000008891689,0.000003381202,0.00002393932,0.08972514,0.005983755,0.9036828,0.0000252934,0.00009776477],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1064977,0.00003543388,0.8894181,0.00003231068,0.00008210053,0.0001531372,0.00001695081,0.0001594276,0.003604912],"genre_scores_gemma":[0.6914507,0.000002260132,0.308447,0.00001264498,0.00001218973,0.000004228721,0.00000580776,0.0000111581,0.00005403484],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.584953,"threshold_uncertainty_score":0.3708233,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.2902512635374343,"score_gpt":0.4371757447731041,"score_spread":0.1469244812356698,"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."}}