{"id":"W4205150711","doi":"10.1111/biom.13625","title":"Ultra-High Dimensional Variable Selection for Doubly Robust Causal Inference","year":2022,"lang":"en","type":"article","venue":"Biometrics","topic":"Advanced Causal Inference Techniques","field":"Mathematics","cited_by":21,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"Canadian Statistical Sciences Institute; Natural Sciences and Engineering Research Council of Canada; National Natural Science Foundation of China","keywords":"Causal inference; Covariate; Estimator; Feature selection; Propensity score matching; Computer science; Confounding; Econometrics; Outcome (game theory); Robustness (evolution); Causal model; Inference; Statistics; Machine learning; Artificial intelligence; Mathematics","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007427273,0.0001945708,0.0002615506,0.001040037,0.0004046092,0.00004234606,0.0002746245,0.0001005653,0.0004607316],"category_scores_gemma":[0.002264007,0.0002007455,0.0000634025,0.004624246,0.0000428883,0.0001860765,0.0001361004,0.0002706132,0.000006709281],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004063261,"about_ca_system_score_gemma":0.0001675934,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00008801991,"about_ca_topic_score_gemma":0.000005875601,"domain_scores_codex":[0.998305,0.00006395351,0.000355475,0.0003586047,0.0005272406,0.0003897144],"domain_scores_gemma":[0.9975309,0.001584905,0.00021758,0.0002638648,0.0003133778,0.00008931269],"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":[0.0001374288,0.0007364746,0.001089499,0.0001507528,0.00008100255,0.000004252917,0.00009069569,0.005868729,0.04987502,0.8976478,0.03821912,0.006099231],"study_design_scores_gemma":[0.001302932,0.001631032,0.0003174546,0.00002593651,0.0001142911,0.00005815472,0.00005332794,0.008647087,0.04178489,0.9007427,0.04443083,0.0008913355],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.02073458,0.00005263868,0.9763577,0.0000893155,0.0004906424,0.0007114355,0.0002370786,0.0005885129,0.0007381222],"genre_scores_gemma":[0.4981327,0.000005212873,0.499905,0.0001297726,0.00009766224,0.0004431507,0.00009513865,0.00004471446,0.001146604],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.4773981,"threshold_uncertainty_score":0.8186164,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1643877230354156,"score_gpt":0.3827650483022176,"score_spread":0.2183773252668021,"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."}}