{"id":"W4399726225","doi":"10.1080/00031305.2024.2368794","title":"High-Dimensional Propensity Score and Its Machine Learning Extensions in Residual Confounding Control","year":2024,"lang":"en","type":"article","venue":"The American Statistician","topic":"Advanced Causal Inference Techniques","field":"Mathematics","cited_by":12,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Propensity score matching; Confounding; Residual; Computer science; Proxy (statistics); Exploit; Causal inference; Parametric statistics; Machine learning; Econometrics; Artificial intelligence; Data mining; Statistics; Algorithm; 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":[0.0005871254,0.0001814529,0.0003951644,0.0001172794,0.000171867,0.00006204726,0.0001149357,0.00002364561,0.00004902867],"category_scores_gemma":[0.001282953,0.0001256859,0.0000201018,0.00027522,0.0003356132,0.0000996306,0.00009714024,0.0005050414,0.0000176601],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000704745,"about_ca_system_score_gemma":0.00006422827,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0006763979,"about_ca_topic_score_gemma":0.0003969213,"domain_scores_codex":[0.9986395,0.0002689905,0.0002865076,0.0002808539,0.0002296313,0.0002945532],"domain_scores_gemma":[0.9973034,0.002248378,0.0001386288,0.0001700152,0.00007175752,0.00006787832],"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":[0.0001990141,0.00005673265,0.002035495,0.0001144343,0.00008403204,0.0005437787,0.0009814316,0.0002190097,0.01283797,0.9527827,0.001108532,0.02903689],"study_design_scores_gemma":[0.001046325,0.001176459,0.05082829,0.00128762,0.0003059626,0.0002937412,0.000760612,0.08463199,0.002227633,0.8556846,0.0006973444,0.001059393],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9411444,0.0003652164,0.05604637,0.00107467,0.00008331317,0.0005521816,0.0001321133,0.0004031305,0.0001986293],"genre_scores_gemma":[0.984448,0.00004180781,0.01485133,0.000332033,0.00005444919,0.00003162512,0.00001079526,0.00003753449,0.0001924919],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.09709805,"threshold_uncertainty_score":0.5125324,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1205700477072864,"score_gpt":0.3918923405365465,"score_spread":0.2713222928292602,"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."}}