{"id":"W3132584191","doi":"10.48550/arxiv.2201.01942","title":"Efficiently Disentangle Causal Representations","year":2022,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Bayesian Modeling and Causal Inference","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Kootenay Association for Science & Technology","funders":"","keywords":"Generalization; Computer science; Contrast (vision); Code (set theory); Adaptation (eye); Artificial intelligence; Source code; Causal model; Sample (material); Machine learning; Theoretical computer science; Algorithm; Mathematics; Set (abstract data type); Statistics; Programming language","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"],"consensus_categories":[],"category_scores_codex":[0.0002151731,0.0002708341,0.0002533701,0.0002415354,0.0003781445,0.0001949101,0.002305044,0.0001461046,0.0002056842],"category_scores_gemma":[0.00002979271,0.0003366394,0.000213844,0.0007631232,0.00009394262,0.0002602839,0.003729761,0.0007537025,0.0001093703],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002435165,"about_ca_system_score_gemma":0.0002986526,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002932423,"about_ca_topic_score_gemma":0.00002741491,"domain_scores_codex":[0.9977453,0.0002035813,0.0002108749,0.001299863,0.0001660673,0.0003743604],"domain_scores_gemma":[0.9977667,0.000101699,0.0001927979,0.001629473,0.0001094433,0.0001998259],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.000009732934,0.0001493387,0.001612389,0.00002409153,0.00004987079,0.0003641024,0.0003618987,0.5799429,0.00003316123,0.4159516,0.00113669,0.0003641538],"study_design_scores_gemma":[0.0002811453,0.0000488302,0.0009199557,0.00002982449,0.0000562535,0.000007647696,0.0001729024,0.9432119,0.00007500266,0.05381891,0.0008692177,0.0005084265],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1053151,0.00005206911,0.8837196,0.0002338967,0.001065109,0.0002146913,0.00004184903,0.0004270865,0.008930596],"genre_scores_gemma":[0.9932581,0.00006556391,0.00190548,0.0001330717,0.00005930127,0.000003542549,0.00005075551,0.00001734811,0.004506905],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.887943,"threshold_uncertainty_score":0.9999086,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0952892448215393,"score_gpt":0.2213295657566847,"score_spread":0.1260403209351454,"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."}}