{"id":"W2519327609","doi":"10.3982/ecta16437","title":"Model Selection for Treatment Choice: Penalized Welfare Maximization","year":2021,"lang":"en","type":"preprint","venue":"Econometrica","topic":"Advanced Causal Inference Techniques","field":"Mathematics","cited_by":14,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Calgary","funders":"Shanxi Scholarship Council of China; Northwestern University","keywords":"Oracle; Regret; Maximization; Selection (genetic algorithm); Covariate; Mathematical optimization; Class (philosophy); Computer science; Mathematics; Econometrics; Machine learning; Artificial intelligence","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":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0001721863,0.0003726005,0.0007185992,0.0007094871,0.0001346838,0.0001506066,0.0002171277,0.0003888944,0.0004123818],"category_scores_gemma":[0.001132818,0.0003820271,0.0002991658,0.0004738528,0.00002006962,0.0001725511,0.0001852213,0.0001717261,0.000004290484],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001258486,"about_ca_system_score_gemma":0.0001805227,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00004884343,"about_ca_topic_score_gemma":0.00006974644,"domain_scores_codex":[0.9983374,0.00004183951,0.000550446,0.0006640065,0.0001150894,0.000291209],"domain_scores_gemma":[0.998327,0.0004019492,0.0003605041,0.0005317274,0.0002906538,0.00008812114],"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":[0.0004460005,0.0057345,0.004816335,0.006112207,0.003562187,0.00001193452,0.003072286,0.1917353,0.001114533,0.5613396,0.01737619,0.204679],"study_design_scores_gemma":[0.001384625,0.000355103,0.0001381306,0.000121883,0.0003779416,0.00000574863,0.00008446493,0.4210391,0.006569875,0.5603808,0.008519404,0.001022998],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.01359239,0.0002649079,0.979286,0.0004627884,0.0001954886,0.001842571,0.0001607207,0.0006350172,0.003560177],"genre_scores_gemma":[0.4447298,0.0003869793,0.5497348,0.00004185865,0.0001669518,0.00233553,0.000640785,0.0001071873,0.001856129],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.4311374,"threshold_uncertainty_score":0.9998631,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.3036878530973369,"score_gpt":0.437126187165306,"score_spread":0.1334383340679691,"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."}}