{"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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0002298927,0.0002813541,0.0003031041,0.0001732465,0.0004116732,0.0002089887,0.0004811863,0.0001135577,0.001175866],"category_scores_gemma":[0.007471581,0.0002710768,0.0001751348,0.0002365417,0.000123843,0.0004582152,0.0001391976,0.0006730023,0.0001009417],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001149685,"about_ca_system_score_gemma":0.00009060157,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002054771,"about_ca_topic_score_gemma":0.000008112226,"domain_scores_codex":[0.9976782,0.0001947865,0.0005601557,0.0006520466,0.0006074316,0.0003074365],"domain_scores_gemma":[0.9968143,0.001608772,0.0004372574,0.0002683297,0.0006837235,0.0001876755],"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":[0.000807004,0.0003757364,0.01442048,0.0000912274,0.0003340143,0.00003520183,0.01488748,0.006120584,0.04121824,0.89101,0.01844097,0.01225907],"study_design_scores_gemma":[0.0049834,0.00272569,0.003288073,0.0008423245,0.0002648896,0.00004452811,0.03120186,0.3415065,0.0398052,0.5321841,0.04108804,0.002065397],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1898557,0.00002436405,0.6462003,0.03767588,0.0009152503,0.002520359,0.0001828612,0.003130229,0.119495],"genre_scores_gemma":[0.9781236,0.00005148509,0.01543433,0.0003079081,0.0003110567,0.0003746253,0.0003079486,0.00006205786,0.00502696],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.7882679,"threshold_uncertainty_score":0.9999741,"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."}}