{"id":"W1527417422","doi":"10.1007/11424918_27","title":"On the Role of the Markov Condition in Causal Reasoning","year":2005,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Bayesian Modeling and Causal Inference","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Saskatchewan","funders":"","keywords":"Conditional independence; Computer science; Causal inference; Markov chain; Inference; Independence (probability theory); Causal model; Markov blanket; Causal structure; Artificial intelligence; Markov process; Theoretical computer science; Causal reasoning; Proxy (statistics); Graph; Markov model; Markov kernel; Markov property; Machine learning; Variable-order Markov model; Mathematics; Econometrics; Psychology; Statistics; Cognition","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":[],"consensus_categories":[],"category_scores_codex":[0.001021252,0.0003170034,0.0003007223,0.000301456,0.0001921321,0.0002080445,0.003252475,0.000214882,0.00002191487],"category_scores_gemma":[0.0001387341,0.0001960252,0.00009940858,0.0005389531,0.0005824422,0.0002399817,0.0007684308,0.000993464,0.0000144711],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001820014,"about_ca_system_score_gemma":0.0004024978,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00005024365,"about_ca_topic_score_gemma":0.0001475966,"domain_scores_codex":[0.9974816,0.00008138544,0.0004139792,0.0007956324,0.0008117182,0.0004156803],"domain_scores_gemma":[0.9976482,0.000646359,0.0002717654,0.001252733,0.0001227167,0.00005827611],"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":[0.00000562685,0.00002844992,0.0001059365,0.000008776957,0.00000512504,0.000008612082,0.0007988462,0.05035933,0.0002434427,0.5002514,0.0000215724,0.4481629],"study_design_scores_gemma":[0.00009353305,0.00006457054,0.000372951,0.0006499162,0.000002977898,0.00001890843,3.45009e-7,0.6031945,0.002157527,0.3929964,0.0002054619,0.0002429167],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.001288342,0.0002756026,0.9878026,0.001507139,0.0005953885,0.0002719198,0.00000410858,0.00004243891,0.008212435],"genre_scores_gemma":[0.9661043,0.00002003491,0.03206779,0.001488211,0.0001608875,0.00000785366,0.000001059206,0.00001514203,0.0001346659],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.964816,"threshold_uncertainty_score":0.7993677,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01176648332578537,"score_gpt":0.233911275412569,"score_spread":0.2221447920867836,"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."}}