{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.03270654,0.0006921778,0.001243071,0.003368448,0.001209162,0.001735327,0.003156635,0.002626936,0.004141483],"category_scores_gemma":[0.1780614,0.0004374968,0.001282781,0.002575037,0.00273634,0.002839528,0.002098298,0.003007847,0.0004664335],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001042321,"about_ca_system_score_gemma":0.002266329,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002052832,"about_ca_topic_score_gemma":0.001802218,"domain_scores_codex":[0.9716231,0.02206411,0.0007275721,0.003137785,0.001903608,0.0005438148],"domain_scores_gemma":[0.8166463,0.1631855,0.006637938,0.007791073,0.004665698,0.001073461],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.001500837,0.0005743451,0.09117769,0.0005097756,0.001164769,0.001011162,0.000718833,0.198265,0.005399166,0.327184,0.00673171,0.3657627],"study_design_scores_gemma":[0.0003265813,0.0004428787,0.009956161,0.00006329999,0.00015459,0.000391143,0.0001746742,0.7889662,0.003524357,0.192189,0.00371456,0.00009646989],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.04177414,0.0001475184,0.9552978,0.0004895097,0.00007527277,0.0002085776,0.0001964234,0.0004278739,0.001382737],"genre_scores_gemma":[0.6040511,0.0001178398,0.3931468,0.0003664806,0.0001208903,0.0006720029,0.0005383996,0.00009799255,0.0008884839],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.03270654,"threshold_uncertainty_score":0.1729708,"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."}}