{"id":"W2916020102","doi":"","title":"A Combinatorial Approach to Causal Inference","year":2019,"lang":"en","type":"article","venue":"arXiv (Cornell University)","topic":"Bayesian Modeling and Causal Inference","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Perimeter Institute","funders":"","keywords":"Causal inference; Inference; Causal consistency; Graphical model; Latent variable; Theoretical computer science; Exploit; Computer science; Causal model; Compatibility (geochemistry); Mathematics; Artificial intelligence; Algorithm; Econometrics; Consistency model","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":[0.01554021,0.001821064,0.002255402,0.007364403,0.003072886,0.007370761,0.004837984,0.003683801,0.0174975],"category_scores_gemma":[0.0501205,0.001959225,0.005551631,0.006311531,0.0117592,0.01466344,0.005755451,0.008955217,0.001804131],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.004249838,"about_ca_system_score_gemma":0.002789002,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002714408,"about_ca_topic_score_gemma":0.002779942,"domain_scores_codex":[0.9817635,0.01125384,0.000926667,0.003018683,0.002600165,0.0004370137],"domain_scores_gemma":[0.9511786,0.03963248,0.002040625,0.004655832,0.001853455,0.0006389002],"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":[0.000008759158,0.00001248756,0.0001245073,0.00007811816,0.00004492366,0.0000568325,0.00007126938,0.004866745,0.00007967372,0.9886531,0.0005824012,0.005421232],"study_design_scores_gemma":[0.000008613489,0.00000574525,0.00004056355,0.00002717188,0.00001474604,0.0000416476,0.00001588071,0.01218713,0.00007500537,0.9853708,0.002204347,0.000008318422],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.001144213,0.0004226376,0.9877643,0.001607195,0.00006396849,0.00006543457,0.0002000094,0.0001126793,0.008619504],"genre_scores_gemma":[0.1547217,0.001765128,0.8343137,0.001290544,0.0007451425,0.0007271091,0.0008105472,0.0002261961,0.00539989],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.0174975,"threshold_uncertainty_score":0.08218551,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0615966859213876,"score_gpt":0.1886087667712033,"score_spread":0.1270120808498157,"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."}}