{"id":"W2147784336","doi":"10.1007/s00038-010-0198-4","title":"Marginal Structural Models: unbiased estimation for longitudinal studies","year":2010,"lang":"en","type":"article","venue":"International Journal of Public Health","topic":"Advanced Causal Inference Techniques","field":"Mathematics","cited_by":35,"is_retracted":false,"has_abstract":false,"ca_institutions":"McGill University","funders":"Canadian Institutes of Health Research","keywords":"Marginal structural model; Confounding; Weighting; Latent variable; Unbiased Estimation; Estimation; Statistics; Econometrics; Inverse probability weighting; Mathematics; Computer science; Medicine; Economics; Estimator","routes":{"ca_aff":true,"ca_fund":true,"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":[0.0433379,0.002778226,0.005052974,0.00377333,0.001365659,0.003112612,0.005124759,0.003518921,0.008777084],"category_scores_gemma":[0.1945898,0.003319727,0.003352028,0.004779895,0.002599795,0.005160725,0.003853057,0.006181139,0.001839381],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001462048,"about_ca_system_score_gemma":0.004362036,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.007431372,"about_ca_topic_score_gemma":0.008935099,"domain_scores_codex":[0.9773373,0.01911976,0.0005685823,0.00168145,0.0009576649,0.0003353075],"domain_scores_gemma":[0.8357973,0.1493693,0.003090203,0.008777069,0.002438361,0.0005278306],"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":[0.0004773137,0.0002255164,0.006281574,0.001043284,0.002250167,0.0004282411,0.0007166353,0.2030356,0.0008289253,0.5616699,0.01168211,0.2113607],"study_design_scores_gemma":[0.00009746991,0.00005434431,0.0007039128,0.0001416632,0.000316448,0.0001105554,0.00007062397,0.4610555,0.0003117098,0.5337144,0.003379075,0.00004437171],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.0009021989,0.0005920996,0.9977261,0.0002055041,0.00003444049,0.00005064563,0.0001289128,0.0002057363,0.0001542487],"genre_scores_gemma":[0.1004209,0.003694863,0.8866944,0.000349916,0.0005039009,0.002157363,0.001424099,0.0005180295,0.004236508],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.0433379,"threshold_uncertainty_score":0.2291954,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.5599853245882525,"score_gpt":0.564452031610165,"score_spread":0.004466707021912542,"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."}}