{"id":"W2995122033","doi":"","title":"Learning Disentangled Representations for CounterFactual Regression","year":2020,"lang":"en","type":"article","venue":"International Conference on Learning Representations","topic":"Advanced Causal Inference Techniques","field":"Mathematics","cited_by":40,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"","keywords":"Counterfactual thinking; Leverage (statistics); Computer science; Observational study; Selection bias; Covariate; Machine learning; Selection (genetic algorithm); Econometrics; Population; Model selection; Regression; Artificial intelligence; Statistics; Mathematics; Psychology","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":[0.01921616,0.001696005,0.002600135,0.002247242,0.0006242189,0.003091718,0.003542276,0.0027404,0.003517062],"category_scores_gemma":[0.06536238,0.0008512179,0.002036056,0.00300602,0.001798126,0.004726478,0.003652858,0.005752333,0.0009987858],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001649087,"about_ca_system_score_gemma":0.001963866,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002224409,"about_ca_topic_score_gemma":0.002287706,"domain_scores_codex":[0.9908924,0.006774746,0.0003545612,0.001036592,0.0006968404,0.0002447865],"domain_scores_gemma":[0.9500248,0.04100853,0.002395305,0.005133995,0.001018795,0.0004186168],"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":[0.0005602501,0.0004587326,0.006669006,0.0005568768,0.0006243375,0.0001942646,0.0004480169,0.3890463,0.0008707336,0.2092816,0.006179619,0.3851102],"study_design_scores_gemma":[0.000046769,0.00005605616,0.0003760964,0.00006870915,0.00004428579,0.00004604515,0.00002434317,0.8266334,0.0003883441,0.1708489,0.001447382,0.00001965609],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.008115768,0.0006762504,0.9893189,0.0005656714,0.00004458834,0.00007674594,0.0002730464,0.0003946022,0.0005344184],"genre_scores_gemma":[0.4508722,0.001397645,0.5392421,0.0009603118,0.000411022,0.000873602,0.003428379,0.000202245,0.002612483],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.01921616,"threshold_uncertainty_score":0.101626,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.279971621243436,"score_gpt":0.4848305298920393,"score_spread":0.2048589086486033,"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."}}