{"id":"W4408783432","doi":"10.3386/w33607","title":"Correcting Endogeneity via Nonparametric Copula Control Functions","year":2025,"lang":"en","type":"report","venue":"National Bureau of Economic Research","topic":"Control Systems and Identification","field":"Engineering","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Social Sciences and Humanities Research Council of Canada; Natural Sciences and Engineering Research Council of Canada","keywords":"Endogeneity; Nonparametric statistics; Copula (linguistics); Econometrics; Statistics; Mathematics; Economics; Computer science","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00446267,0.0002193196,0.0005987569,0.001885156,0.0001666249,0.0001004068,0.0003507561,0.0004013777,0.000283724],"category_scores_gemma":[0.001672836,0.0002522219,0.0002484058,0.0005337383,0.00006685188,0.0001266637,0.00005320537,0.0007684075,0.0002461497],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.003167527,"about_ca_system_score_gemma":0.001547834,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003389821,"about_ca_topic_score_gemma":0.000646856,"domain_scores_codex":[0.9969787,0.0001426579,0.0009768009,0.0004176038,0.001119175,0.0003650339],"domain_scores_gemma":[0.9955917,0.00150305,0.0002216621,0.0003769744,0.0022191,0.00008748755],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0001087026,0.0002001681,0.004503687,0.00224993,0.002586497,0.000006022732,0.00005626378,0.125014,0.00480424,0.01040348,0.7259884,0.1240786],"study_design_scores_gemma":[0.005596887,0.0002526095,0.01299645,0.001221511,0.0004378653,0.0002565993,0.0001900166,0.4730891,0.002162078,0.03680122,0.4649617,0.002033992],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"other","genre_gemma":"empirical","genre_scores_codex":[0.00379618,0.005600172,0.003839319,0.0001346479,0.008559024,0.001829936,0.0006238999,0.0001721175,0.9754447],"genre_scores_gemma":[0.9768876,0.0002875087,0.0000270288,0.000003271655,0.001096284,0.0002846167,0.0005843044,0.00003929638,0.02079008],"genre_candidate":"other","genre_consensus":null,"teacher_disagreement_score":0.9730914,"threshold_uncertainty_score":0.999993,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.2117205106864516,"score_gpt":0.4493014625980833,"score_spread":0.2375809519116316,"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."}}