{"id":"W2766219675","doi":"10.1002/sim.7527","title":"Collaborative targeted learning using regression shrinkage","year":2017,"lang":"en","type":"article","venue":"Statistics in Medicine","topic":"Advanced Causal Inference Techniques","field":"Mathematics","cited_by":8,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université de Montréal","funders":"Natural Sciences and Engineering Research Council of Canada; Canadian Institutes of Health Research; Université de Montréal","keywords":"Covariate; Causal inference; Propensity score matching; Estimator; Statistics; Regression; Regression analysis; Inference; Computer science; Stepwise regression; Mean squared error; Model selection; Outcome (game theory); Econometrics; Mathematics; Artificial intelligence","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[{"model":"gpt","categories":[],"domain":null,"study_design":"simulation_or_modeling","genre":"methods","about_ca_system":false,"about_ca_topic":false,"confidence":"high","status":"direct model label, unvalidated"},{"model":"grok","categories":[],"domain":null,"study_design":"simulation_or_modeling","genre":"methods","about_ca_system":false,"about_ca_topic":false,"confidence":"high","status":"direct model label, unvalidated"},{"model":"opus","categories":[],"domain":null,"study_design":"simulation_or_modeling","genre":"methods","about_ca_system":false,"about_ca_topic":false,"confidence":"high","status":"direct model label, unvalidated"}],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.01251917,0.001215977,0.002591243,0.001462225,0.0006373856,0.001326297,0.002792407,0.001845697,0.002673747],"category_scores_gemma":[0.04545935,0.0008906438,0.001799846,0.001490025,0.001661999,0.002114346,0.003521597,0.002635943,0.0006716912],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001131307,"about_ca_system_score_gemma":0.002158216,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0030088,"about_ca_topic_score_gemma":0.003198758,"domain_scores_codex":[0.994111,0.003798953,0.0002501636,0.0008164989,0.0007785208,0.0002449131],"domain_scores_gemma":[0.9712924,0.02306952,0.001358984,0.00254656,0.001400716,0.000331811],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0002761,0.0001744085,0.003977405,0.0002435438,0.0003035618,0.0002119633,0.0002590205,0.7114103,0.001648229,0.08624688,0.0032997,0.1919488],"study_design_scores_gemma":[0.0000426579,0.00005021926,0.0001970394,0.00001857067,0.00002601815,0.00002618687,0.000009776812,0.9650024,0.0005278084,0.03347688,0.0006136841,0.000008824043],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.004191968,0.0001123932,0.994827,0.0001652974,0.00001538779,0.00004429395,0.00002781913,0.0002023141,0.0004135144],"genre_scores_gemma":[0.4274494,0.0005461033,0.5663723,0.0005946813,0.0001962659,0.0007871857,0.000475832,0.0001834615,0.003394767],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.01251917,"threshold_uncertainty_score":0,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1173474174511691,"score_gpt":0.4830962113690352,"score_spread":0.3657487939178661,"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."}}