{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.03900199,0.00128599,0.002709119,0.003396241,0.001637859,0.002471825,0.003887087,0.002013025,0.00562932],"category_scores_gemma":[0.1238594,0.001187454,0.003143508,0.003794915,0.004034798,0.00343983,0.006063677,0.005063324,0.001162875],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001329403,"about_ca_system_score_gemma":0.003226471,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002347807,"about_ca_topic_score_gemma":0.002146237,"domain_scores_codex":[0.9750284,0.01957802,0.0008134943,0.001806141,0.002364506,0.0004094432],"domain_scores_gemma":[0.9056083,0.07214329,0.004883063,0.01358075,0.003037984,0.0007465491],"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":[0.0002032387,0.0001705566,0.005669164,0.0005339191,0.000615886,0.0004419811,0.0003895349,0.1010157,0.001297992,0.7511842,0.004724274,0.1337534],"study_design_scores_gemma":[0.00008421266,0.00008403392,0.001155841,0.00009372819,0.00007592874,0.0001410104,0.00004307906,0.4463623,0.0008158011,0.5469329,0.004162245,0.00004884706],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.001211642,0.0001489011,0.997982,0.0002003756,0.00002507863,0.00004079703,0.00006819633,0.0001014739,0.0002214472],"genre_scores_gemma":[0.1309112,0.0009493566,0.8635929,0.0006084811,0.0003854763,0.001150652,0.0008073591,0.0001688973,0.001425725],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.03900199,"threshold_uncertainty_score":0.2062647,"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."}}