{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.02316558,0.001594395,0.00332098,0.001300528,0.0007506501,0.002951456,0.003248235,0.00259157,0.00317376],"category_scores_gemma":[0.06012248,0.0009044369,0.001435073,0.001580209,0.003537315,0.003393306,0.002587825,0.005955307,0.000669584],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00278952,"about_ca_system_score_gemma":0.002637887,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001801486,"about_ca_topic_score_gemma":0.001293026,"domain_scores_codex":[0.9757623,0.01916348,0.000432758,0.001987206,0.001976213,0.0006781379],"domain_scores_gemma":[0.9526709,0.04147644,0.001815045,0.002338456,0.00106922,0.0006299658],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.0002800499,0.0003195058,0.002040133,0.0002870228,0.0002990122,0.0001483815,0.0002004959,0.5471107,0.0005944297,0.3865716,0.004209871,0.05793887],"study_design_scores_gemma":[0.00007275163,0.00009176702,0.0002154158,0.00004840798,0.00002226492,0.00003865996,0.00001281655,0.7712025,0.000376631,0.2266931,0.001209946,0.00001562903],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.006017318,0.0003946331,0.990362,0.001365471,0.00005034657,0.000111564,0.00007717338,0.00009543274,0.001526011],"genre_scores_gemma":[0.4818801,0.001383626,0.50632,0.001939421,0.0006159892,0.001367759,0.0006733234,0.000243664,0.005576216],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.02316558,"threshold_uncertainty_score":0.1225128,"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."}}