{"id":"W2970371236","doi":"10.1002/bimj.201600228","title":"Inverse‐probability‐of‐treatment weighted estimation of causal parameters in the presence of error‐contaminated and time‐dependent confounders","year":2019,"lang":"en","type":"article","venue":"Biometrical Journal","topic":"Advanced Causal Inference Techniques","field":"Mathematics","cited_by":12,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"Natural Sciences and Engineering Research Council of Canada; Canadian Institutes of Health Research","keywords":"Marginal structural model; Causal inference; Confounding; Estimation; Statistics; Inference; Econometrics; Inverse probability; Mathematics; Observational error; Computer science; Bayesian probability; Artificial intelligence; Economics","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":[0.04994556,0.001857755,0.003439035,0.002512262,0.0008811258,0.002193741,0.004593951,0.003194077,0.006595137],"category_scores_gemma":[0.1902824,0.001067282,0.003535198,0.003751971,0.002955864,0.004653193,0.003477101,0.004812252,0.0006941772],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001644973,"about_ca_system_score_gemma":0.004053599,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004494695,"about_ca_topic_score_gemma":0.003614845,"domain_scores_codex":[0.9735734,0.02132815,0.0007696682,0.002213122,0.001649536,0.0004661227],"domain_scores_gemma":[0.8747551,0.1070917,0.005482933,0.009641162,0.002671992,0.0003571613],"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":[0.0004567648,0.000279063,0.01001284,0.001369593,0.001643746,0.0005207455,0.0008758086,0.171836,0.001242365,0.5023798,0.002647326,0.3067359],"study_design_scores_gemma":[0.0001545613,0.0002209702,0.002897888,0.0002985133,0.0006332718,0.0003393454,0.0001577263,0.4914187,0.001621193,0.4963022,0.005863626,0.00009193007],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.002875393,0.0003330892,0.9958287,0.0001749958,0.00004332566,0.0001780453,0.00007819009,0.00006905709,0.0004190617],"genre_scores_gemma":[0.2083696,0.001506766,0.7841697,0.0004561929,0.0001956683,0.001988955,0.0005668659,0.0001279814,0.002618261],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.04994556,"threshold_uncertainty_score":0.2641405,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.169679660748653,"score_gpt":0.3903522632045267,"score_spread":0.2206726024558738,"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."}}