{"id":"W2103171295","doi":"10.3390/ijerph120809391","title":"Model Averaging for Improving Inference from Causal Diagrams","year":2015,"lang":"en","type":"article","venue":"International Journal of Environmental Research and Public Health","topic":"Advanced Causal Inference Techniques","field":"Mathematics","cited_by":6,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University","funders":"National Center for Research Resources; National Institute of Child Health and Human Development; National Cancer Institute; March of Dimes Foundation; University of North Carolina at Chapel Hill; Canada Research Chairs; Centers for Disease Control and Prevention; National Institute of Diabetes and Digestive and Kidney Diseases; Electric Power Research Institute","keywords":"Causal inference; Spurious relationship; Weighting; Model selection; Computer science; A priori and a posteriori; Bootstrapping (finance); Inference; Directed acyclic graph; Causal model; Variance (accounting); Econometrics; Selection (genetic algorithm); Statistics; Machine learning; Mathematics; Algorithm; Artificial intelligence; Medicine","routes":{"ca_aff":true,"ca_fund":true,"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":[0.0514475,0.002626495,0.00305062,0.008481381,0.001566293,0.003230641,0.003675735,0.002039681,0.004278609],"category_scores_gemma":[0.2257217,0.001825619,0.004766381,0.006588196,0.002679697,0.005939183,0.004852253,0.00537961,0.0008232837],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002224712,"about_ca_system_score_gemma":0.003427554,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005941255,"about_ca_topic_score_gemma":0.00565891,"domain_scores_codex":[0.959133,0.03146879,0.001909237,0.003041012,0.004076756,0.0003713031],"domain_scores_gemma":[0.7982719,0.1807185,0.004764837,0.01166475,0.004025749,0.0005543393],"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":[0.0001583953,0.0001052032,0.004525858,0.001191432,0.001320922,0.0002900324,0.0008061535,0.2829461,0.00159095,0.4159389,0.003675881,0.2874502],"study_design_scores_gemma":[0.00003836316,0.00006924105,0.0004935024,0.0001343068,0.000187266,0.00007790008,0.00004695331,0.4323216,0.000979514,0.5596905,0.005922955,0.0000378966],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.0008193408,0.0004245085,0.9979417,0.0001601503,0.00002699013,0.00003759738,0.00006137688,0.0002379525,0.0002903828],"genre_scores_gemma":[0.04617235,0.001103852,0.9511495,0.0001760711,0.0001458763,0.0003396294,0.0003462817,0.0001920645,0.0003743875],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.0514475,"threshold_uncertainty_score":0.2720836,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.4529308440761285,"score_gpt":0.5176326384787479,"score_spread":0.06470179440261936,"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."}}