{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002698243,0.00009769265,0.0001874911,0.0002177062,0.00008276985,0.0001319302,0.0003792181,0.00005080678,0.0000267744],"category_scores_gemma":[0.002288193,0.00008399882,0.00003899398,0.00004520653,0.0001473835,0.0006011662,0.0002022421,0.0004045547,0.000002004208],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007239429,"about_ca_system_score_gemma":0.0005435858,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00009535073,"about_ca_topic_score_gemma":0.00003587825,"domain_scores_codex":[0.9978933,0.0001174507,0.0004499196,0.0001548708,0.001014909,0.0003695178],"domain_scores_gemma":[0.9978749,0.0009454216,0.0002713969,0.0001138445,0.000246568,0.000547923],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.0005988994,0.002318946,0.03207922,0.00009503988,0.0003026192,0.00007899503,0.007037718,0.0001326904,0.007581222,0.131874,0.006616577,0.811284],"study_design_scores_gemma":[0.001130942,0.0009606615,0.0009959876,0.00007000504,0.000003033016,0.00003671646,0.001480082,0.01464508,0.0004784738,0.9774854,0.00257399,0.0001395917],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5110089,0.0003412418,0.4832196,0.004715254,0.0001427987,0.0002497419,0.0001403396,0.00001871258,0.0001634377],"genre_scores_gemma":[0.939001,0.0003100397,0.06014943,0.0002025653,0.0002185565,0.00001466229,0.00001672864,0.00001544549,0.00007161943],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.8456114,"threshold_uncertainty_score":0.3425373,"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."}}