{"id":"W2713091059","doi":"10.1093/ije/dyx023","title":"Assessing the impact of unmeasured confounding for binary outcomes using confounding functions","year":2017,"lang":"en","type":"article","venue":"International Journal of Epidemiology","topic":"Advanced Causal Inference Techniques","field":"Mathematics","cited_by":32,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University","funders":"","keywords":"Confounding; Causal inference; Econometrics; Observational study; Statistics; Inference; Instrumental variable; Odds; Information bias; Computer science; Selection bias; Mathematics; Logistic regression; Artificial intelligence","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":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.1807718,0.002462076,0.003825398,0.006044236,0.00169842,0.006420573,0.004384052,0.004532823,0.007578472],"category_scores_gemma":[0.458331,0.00119304,0.005237977,0.005762899,0.007102815,0.01035876,0.00655196,0.006168539,0.001318716],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002571903,"about_ca_system_score_gemma":0.004349393,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003388028,"about_ca_topic_score_gemma":0.00153388,"domain_scores_codex":[0.864884,0.1074882,0.005910674,0.007096532,0.01343518,0.001185477],"domain_scores_gemma":[0.416639,0.5345336,0.01982976,0.02289503,0.005573248,0.0005293508],"domain_codex":null,"domain_gemma":"methods","domain_candidate":"methods","domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.0002828802,0.0001688194,0.02903294,0.002911023,0.002379425,0.0006090383,0.001765981,0.05567053,0.0009279714,0.7074034,0.004054544,0.1947933],"study_design_scores_gemma":[0.00007071658,0.0001665404,0.006345838,0.000945528,0.00047352,0.0005407511,0.000234337,0.09806084,0.001727633,0.8790774,0.01225942,0.00009756143],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.004140235,0.0008785029,0.9913948,0.001064294,0.00006828314,0.0002020933,0.0002590466,0.0001834017,0.001809263],"genre_scores_gemma":[0.217315,0.002285078,0.7742236,0.001292327,0.0003686294,0.00163706,0.0005730712,0.0003086839,0.001996527],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.8192282,"threshold_uncertainty_score":0.9560243,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.593533639452308,"score_gpt":0.6345018749151049,"score_spread":0.04096823546279693,"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."}}