{"id":"W3031898476","doi":"10.1145/3318464.3389759","title":"Causal Relational Learning","year":2020,"lang":"en","type":"article","venue":"","topic":"Bayesian Modeling and Causal Inference","field":"Computer Science","cited_by":37,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Alberta Innovates - Technology Futures; National Institutes of Health; National Science Foundation","keywords":"Causal inference; Computer science; Relational database; Causal structure; Inference; Datalog; Causality (physics); Data science; Theoretical computer science; Artificial intelligence; Machine learning; Data mining; Mathematics; Econometrics","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00005726928,0.00004210203,0.00004261446,0.00001099923,0.00005861061,0.00005118378,0.0002053903,0.00002304396,0.00007945263],"category_scores_gemma":[0.00004121349,0.00003762003,0.00001761053,0.0001326748,0.000008403429,0.0001962813,0.00008184953,0.0001295605,0.0004020218],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000004471626,"about_ca_system_score_gemma":0.00003375171,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000004370059,"about_ca_topic_score_gemma":2.759911e-7,"domain_scores_codex":[0.9995359,0.00002067559,0.00007453078,0.0001598435,0.0001174004,0.00009160481],"domain_scores_gemma":[0.9997738,0.00002742622,0.00001580234,0.00007058703,0.00002834851,0.00008405832],"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":[0.000001052658,0.000004662621,0.000896134,0.000001753528,0.000003167525,0.000004448044,0.0004207724,0.004712324,0.0005072685,0.9791839,0.00183846,0.01242613],"study_design_scores_gemma":[0.00006476768,0.00004271054,0.0006485538,0.000001943713,7.349884e-7,0.000003148216,0.000008372309,0.9878227,0.0002629817,0.004148485,0.006918438,0.00007715305],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.001570216,0.00001575362,0.9659197,0.007650787,0.00004098875,0.00001382997,7.497022e-8,0.0002724159,0.02451629],"genre_scores_gemma":[0.9082028,0.000001660908,0.08946271,0.001701371,0.00005197747,9.76643e-7,7.894095e-7,0.00000216871,0.0005755275],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9831104,"threshold_uncertainty_score":0.5167311,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0581413985270197,"score_gpt":0.2462652500311113,"score_spread":0.1881238515040916,"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."}}