{"id":"W3011337113","doi":"10.1177/0962280221995963","title":"Causal simulation experiments: Lessons from bias amplification","year":2021,"lang":"en","type":"preprint","venue":"Statistical Methods in Medical Research","topic":"Advanced Causal Inference Techniques","field":"Mathematics","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"Jewish General Hospital; McGill University","funders":"Canadian Institutes of Health Research","keywords":"Causal inference; Estimator; Context (archaeology); Computer science; Confounding; Econometrics; Inference; Parametric statistics; Set (abstract data type); Class (philosophy); Selection bias; Sensitivity (control systems); Artificial intelligence; Statistics; Mathematics; Engineering","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":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.2096983,0.001742576,0.002460415,0.001696196,0.001578003,0.003934579,0.004733034,0.005503783,0.009037985],"category_scores_gemma":[0.6408678,0.00117637,0.002365685,0.002064868,0.009724843,0.01001848,0.006202068,0.007703622,0.0006541564],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002165411,"about_ca_system_score_gemma":0.003090366,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001477315,"about_ca_topic_score_gemma":0.0009477852,"domain_scores_codex":[0.7759513,0.208807,0.003325033,0.004946655,0.006120299,0.0008497918],"domain_scores_gemma":[0.1494284,0.8053609,0.009246688,0.03082401,0.004432873,0.0007071683],"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":[0.0008843899,0.0002970712,0.006441546,0.0009445848,0.0009737895,0.0002969991,0.001088681,0.06785407,0.0004630092,0.8625337,0.003087311,0.05513491],"study_design_scores_gemma":[0.0003365696,0.0001815792,0.0005175627,0.0002087032,0.0001695183,0.0001083653,0.00008765337,0.07694624,0.0005278499,0.918328,0.00255268,0.00003514133],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.02092341,0.001933177,0.9574057,0.00978376,0.0004127231,0.0006755229,0.00026754,0.0003894689,0.008208675],"genre_scores_gemma":[0.6331866,0.001829109,0.3538792,0.005312227,0.0006766361,0.00289721,0.0002444039,0.0002169457,0.00175759],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.2096983,"threshold_uncertainty_score":0.9745827,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.8123613022187561,"score_gpt":0.7422349751842946,"score_spread":0.07012632703446153,"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."}}