{"id":"W2950335439","doi":"10.1111/biom.13104","title":"Model Selection for G-Estimation of Dynamic Treatment Regimes","year":2019,"lang":"en","type":"article","venue":"Biometrics","topic":"Advanced Causal Inference Techniques","field":"Mathematics","cited_by":17,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University; University of Waterloo","funders":"Fonds de Recherche du Québec - Santé; Natural Sciences and Engineering Research Council of Canada; National Institute of Mental Health; University of Waterloo","keywords":"Selection (genetic algorithm); Computer science; Model selection; Identification (biology); Variety (cybernetics); Function (biology); Information Criteria; Estimation; Quadratic equation; Maximum likelihood; Mathematical optimization; Machine learning; Data mining; Econometrics; Artificial intelligence; Mathematics; Statistics; Biology","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":[0.02447891,0.001546348,0.002960622,0.002161728,0.0009373102,0.001793068,0.003121042,0.002649024,0.006342371],"category_scores_gemma":[0.08025454,0.001086555,0.002736472,0.002314947,0.002625904,0.002202431,0.002911935,0.004755678,0.001240929],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002240701,"about_ca_system_score_gemma":0.003853485,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.006624273,"about_ca_topic_score_gemma":0.005393631,"domain_scores_codex":[0.9810622,0.01601438,0.0004069034,0.001381467,0.0007997155,0.0003353673],"domain_scores_gemma":[0.9516759,0.04341742,0.001593707,0.001883864,0.001121344,0.0003077703],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.0002445481,0.0001243297,0.003920524,0.0003935475,0.0005194718,0.000320511,0.000391613,0.4918854,0.0007450161,0.4060632,0.004832085,0.09055979],"study_design_scores_gemma":[0.00007387529,0.00008731591,0.0005831279,0.0000726991,0.00005966882,0.00004913849,0.00003966693,0.7750694,0.0002973979,0.2213051,0.002331418,0.000031133],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.002872152,0.0001561233,0.9957241,0.0004468563,0.00002531833,0.0001121069,0.000121356,0.0001147872,0.000427251],"genre_scores_gemma":[0.2638412,0.0009715458,0.7265736,0.0008821198,0.0002351302,0.00226142,0.001298704,0.0002458844,0.003690471],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.02447891,"threshold_uncertainty_score":0.1294584,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1758216006061143,"score_gpt":0.4371313960581764,"score_spread":0.261309795452062,"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."}}