{"id":"W2278177624","doi":"10.1515/ijb-2015-0017","title":"Variable Selection for Confounder Control, Flexible Modeling and Collaborative Targeted Minimum Loss-Based Estimation in Causal Inference","year":2015,"lang":"en","type":"article","venue":"The International Journal of Biostatistics","topic":"Advanced Causal Inference Techniques","field":"Mathematics","cited_by":38,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université de Montréal; Polytechnique Montréal","funders":"National Institute of Allergy and Infectious Diseases","keywords":"Causal inference; Propensity score matching; Estimator; Computer science; Covariate; Inverse probability weighting; Inverse probability; Average treatment effect; Econometrics; Marginal structural model; Inference; Statistics; Nonparametric statistics; Weighting; Selection bias; Machine learning; Artificial intelligence; Mathematics; Posterior probability","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001005245,0.000124703,0.0002253109,0.0001714735,0.00004363562,0.00009092967,0.0002059006,0.00006598367,0.000008461917],"category_scores_gemma":[0.004449593,0.00009648073,0.00002080773,0.0001545299,0.00008082453,0.0002641035,0.00002226177,0.000183422,5.810971e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002567866,"about_ca_system_score_gemma":0.0005617192,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003236021,"about_ca_topic_score_gemma":0.00003553757,"domain_scores_codex":[0.9987447,0.00007787067,0.0005543153,0.00009797748,0.0003906607,0.0001345233],"domain_scores_gemma":[0.9952593,0.001688934,0.0004809624,0.00006815406,0.002440766,0.00006185321],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.001446815,0.0001678025,0.0008131118,0.00003872938,0.0001711207,0.00001282841,0.0007538931,0.2061673,0.003358133,0.7843611,0.001497768,0.001211373],"study_design_scores_gemma":[0.00110245,0.0001970141,0.00001368406,0.00006404777,0.00002684974,0.000018136,0.0001349463,0.5023753,0.00146058,0.4944896,0.00005250771,0.00006487279],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01223103,0.00004414112,0.9863669,0.0006363164,0.0002214916,0.0002798103,0.0001199071,0.00002458354,0.0000758387],"genre_scores_gemma":[0.5997656,0.000006641527,0.399994,0.0001424889,0.00004666124,0.00001068298,0.00001004488,0.000009859188,0.00001405235],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.5875345,"threshold_uncertainty_score":0.53269,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.100035232748242,"score_gpt":0.4231658834685216,"score_spread":0.3231306507202796,"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."}}