{"id":"W4401357333","doi":"10.1109/tcyb.2024.3431636","title":"Bayesian-Based Causal Structure Inference With a Domain Knowledge Prior for Stable and Interpretable Soft Sensing","year":2024,"lang":"en","type":"article","venue":"IEEE Transactions on Cybernetics","topic":"Fault Detection and Control Systems","field":"Engineering","cited_by":20,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"National Natural Science Foundation of China","keywords":"Interpretability; Causality (physics); Artificial intelligence; Machine learning; Computer science; Causal inference; Inference; Spurious relationship; Stability (learning theory); Bayesian probability; Causal structure; Causal model; Bayesian inference; Domain knowledge; Domain (mathematical analysis); Data mining; Econometrics; Mathematics; Statistics","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":[0.00116381,0.001084705,0.000814284,0.0007246876,0.0003960733,0.0009486615,0.001246049,0.001262673,0.002265947],"category_scores_gemma":[0.005405041,0.0006895015,0.001061182,0.0006621409,0.0008960975,0.002141372,0.001361004,0.002753927,0.0004581192],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008533173,"about_ca_system_score_gemma":0.00137492,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005814957,"about_ca_topic_score_gemma":0.007154102,"domain_scores_codex":[0.9995242,0.0001222488,0.00003217431,0.0001625885,0.000107027,0.00005182417],"domain_scores_gemma":[0.9983104,0.001186979,0.0001525881,0.0001091311,0.0001964027,0.00004456186],"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":[0.0001620662,0.0001015972,0.001435153,0.0002414084,0.0001241251,0.0002016398,0.0002028118,0.7769003,0.01095814,0.03337639,0.001697446,0.174599],"study_design_scores_gemma":[0.000004520457,0.00001100345,0.000127088,0.000008662966,0.000007227446,0.00001179019,0.000005870327,0.9881996,0.0008732835,0.01050354,0.0002412171,0.000006210249],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.008786874,0.0002576664,0.9895096,0.0002386844,0.00003081816,0.00001690547,0.00008461471,0.0003031036,0.0007717394],"genre_scores_gemma":[0.7418883,0.00081403,0.2521502,0.0005570711,0.000152963,0.0001809467,0.0006391577,0.0001786324,0.003438519],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.005814957,"threshold_uncertainty_score":0.01156223,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.006348424716276484,"score_gpt":0.2263576111482624,"score_spread":0.2200091864319859,"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."}}