{"id":"W2278756228","doi":"10.1093/ije/dyv135","title":"Imputation approaches for potential outcomes in causal inference","year":2015,"lang":"en","type":"article","venue":"International Journal of Epidemiology","topic":"Advanced Causal Inference Techniques","field":"Mathematics","cited_by":71,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University","funders":"National Institutes of Health","keywords":"Causal inference; Inference; Imputation (statistics); Missing data; Computer science; Intuition; Causal model; Causal structure; Machine learning; Econometrics; Data science; Artificial intelligence; Psychology; Mathematics; Statistics; Cognitive science","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":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.07265433,0.002275552,0.003130991,0.004757414,0.00174587,0.004018356,0.006884156,0.005620551,0.007918798],"category_scores_gemma":[0.1860034,0.001503375,0.004932718,0.00676946,0.008510897,0.008962652,0.005535312,0.0151194,0.001902923],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00352263,"about_ca_system_score_gemma":0.004671331,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003296473,"about_ca_topic_score_gemma":0.002043258,"domain_scores_codex":[0.9381766,0.05148205,0.001782892,0.003214855,0.004701836,0.0006416962],"domain_scores_gemma":[0.7555254,0.2222392,0.005570001,0.01083824,0.005061543,0.0007655495],"domain_codex":null,"domain_gemma":"methods","domain_candidate":"methods","domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.00003176149,0.00003093769,0.0006695465,0.0004302684,0.0002190469,0.0001604686,0.0003548978,0.01289203,0.00007284784,0.9544307,0.003411887,0.02729566],"study_design_scores_gemma":[0.00002166754,0.00001246557,0.00008610376,0.0001191216,0.0000332021,0.00007164387,0.0000281832,0.02154777,0.00005429378,0.9743261,0.00368693,0.00001254332],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.0005505727,0.002688859,0.9913585,0.003083101,0.0001839873,0.00006596959,0.00009771697,0.00007887323,0.001892403],"genre_scores_gemma":[0.0762272,0.007905409,0.9069659,0.002632936,0.001733484,0.001143698,0.0003530563,0.0001806845,0.002857783],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.9273457,"threshold_uncertainty_score":0.3842374,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.4996990568216211,"score_gpt":0.5278363928453237,"score_spread":0.02813733602370255,"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."}}