{"id":"W4206377948","doi":"10.1016/j.mlwa.2021.100235","title":"A causal direction test for heterogeneous populations","year":2021,"lang":"en","type":"article","venue":"Machine Learning with Applications","topic":"Bayesian Modeling and Causal Inference","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University; Polytechnique Montréal; Huawei Technologies (Canada)","funders":"","keywords":"Computer science; Statistic; Test statistic; Causal structure; Machine learning; Homogeneity (statistics); Data mining; Artificial intelligence; Probabilistic logic; Cluster analysis; Causal model; Population; Multivariate statistics; Graphical model; Statistical hypothesis testing; Statistics; Mathematics","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":[],"consensus_categories":[],"category_scores_codex":[0.00009181465,0.000107053,0.000103345,0.00004988669,0.0004674917,0.0001477019,0.0002074546,0.00004113202,0.000007597825],"category_scores_gemma":[0.00007068204,0.00009988996,0.00004078353,0.0004297621,0.00001976644,0.0001065064,0.00005479506,0.0001677691,0.00001363787],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002722533,"about_ca_system_score_gemma":0.0000798342,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00004030353,"about_ca_topic_score_gemma":0.0001305931,"domain_scores_codex":[0.9991519,0.00003296728,0.0001460233,0.0003700648,0.0001146452,0.0001844142],"domain_scores_gemma":[0.9991727,0.0001297807,0.00007155156,0.0003589419,0.0001891048,0.00007794319],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00001941682,0.0008179727,0.04966461,0.00009525396,0.00009184971,0.00001770754,0.0007466902,0.2213285,0.01040289,0.4280137,0.0005803581,0.288221],"study_design_scores_gemma":[0.0003397077,0.0001558261,0.001498334,0.00001910445,0.00002906074,0.0001616128,0.000010099,0.9336908,0.001906902,0.005885398,0.05601382,0.0002893485],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.001137933,0.0001759554,0.9958752,0.001488423,0.00003612353,0.0002278236,0.00001026178,0.0003692839,0.0006790454],"genre_scores_gemma":[0.7813318,0.000007415987,0.2165584,0.0001461199,0.00006467386,0.000576961,0.00009219682,0.00001500447,0.00120747],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.7801938,"threshold_uncertainty_score":0.4073394,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02479839762350775,"score_gpt":0.2787180526703668,"score_spread":0.253919655046859,"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."}}