{"id":"W4408320751","doi":"10.1002/cjs.70000","title":"Sample empirical likelihood methods for causal inference","year":2025,"lang":"en","type":"article","venue":"Canadian Journal of Statistics","topic":"Advanced Causal Inference Techniques","field":"Mathematics","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Causal inference; Inference; Empirical likelihood; Sample (material); Econometrics; Computer science; Statistics; Mathematics; Artificial intelligence","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":true,"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":[0.06497896,0.002364695,0.003577631,0.006123629,0.001233524,0.003907899,0.006075066,0.003213443,0.0137257],"category_scores_gemma":[0.2494435,0.001640684,0.002898725,0.006295165,0.005444408,0.00609357,0.00436872,0.00789956,0.00175691],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.003274266,"about_ca_system_score_gemma":0.003804391,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.006196783,"about_ca_topic_score_gemma":0.003851897,"domain_scores_codex":[0.9577995,0.03640832,0.000965079,0.001708512,0.002725259,0.0003934504],"domain_scores_gemma":[0.6918645,0.2889913,0.004957195,0.009194745,0.004357016,0.0006352856],"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":[0.00008335045,0.00009676413,0.001566628,0.0004730397,0.0003244462,0.0002088696,0.0003104998,0.0860839,0.0001703347,0.8439171,0.002872696,0.06389248],"study_design_scores_gemma":[0.00006612951,0.00003364089,0.0003466033,0.0001450096,0.00004478282,0.00006942412,0.00006445578,0.337758,0.000222154,0.6576124,0.003610413,0.00002702557],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.0005272467,0.0003131363,0.998077,0.000195243,0.0000294589,0.00008396814,0.00008331717,0.0001188452,0.0005718182],"genre_scores_gemma":[0.09519302,0.001513851,0.8966361,0.0003002101,0.0003809134,0.002030483,0.0007207541,0.0002981424,0.002926584],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.06497896,"threshold_uncertainty_score":0.3436457,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.209577783021491,"score_gpt":0.5161440346327254,"score_spread":0.3065662516112344,"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."}}