{"id":"W4392019626","doi":"10.48550/arxiv.2402.13196","title":"Practical Kernel Tests of Conditional Independence","year":2024,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Statistical Methods and Inference","field":"Mathematics","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"Okanagan University College; University of British Columbia, Okanagan Campus; University of British Columbia; McGill University; Mila - Quebec Artificial Intelligence Institute","funders":"Natural Sciences and Engineering Research Council of Canada; Canadian Institute for Advanced Research","keywords":"Conditional independence; Independence (probability theory); Kernel (algebra); Computer science; Econometrics; Artificial intelligence; Statistics; Mathematics; Discrete mathematics","routes":{"ca_aff":true,"ca_fund":true,"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":[0.0004251501,0.0002359678,0.0004064312,0.0001363115,0.00003956288,0.00003069277,0.0003381368,0.0003761982,0.0007051094],"category_scores_gemma":[0.002288039,0.0002425607,0.0001614834,0.0002296803,0.0002757038,0.00005239883,0.001044624,0.001110135,0.0001387178],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00009712238,"about_ca_system_score_gemma":0.0004183469,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00004812908,"about_ca_topic_score_gemma":0.00001662986,"domain_scores_codex":[0.9985259,0.0001746831,0.0002898292,0.0006183158,0.0001734415,0.0002177851],"domain_scores_gemma":[0.9964883,0.00234861,0.0002485918,0.0005011798,0.0002769337,0.0001364057],"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":[0.00002845944,0.0001348656,0.000537861,0.0005221597,0.000104497,0.0003737886,0.00004395554,0.0002559141,0.0000578865,0.9959041,0.001925901,0.0001106058],"study_design_scores_gemma":[0.0001552696,0.00005105686,0.001620649,0.0002708151,0.0002396986,0.0000141827,0.00006176563,0.0210402,0.0001633615,0.9760796,0.00006239776,0.0002410283],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2276608,0.000032163,0.7500706,0.0001755603,0.0005513668,0.0003550093,0.0005603874,0.0001559003,0.02043825],"genre_scores_gemma":[0.9306795,0.00002701909,0.06799623,0.00002711346,0.00006539843,0.000001156408,0.00001433143,0.00002229268,0.001166964],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.7030187,"threshold_uncertainty_score":0.9891339,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.2700438346204136,"score_gpt":0.3316098985148516,"score_spread":0.06156606389443792,"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."}}