{"id":"W3107334064","doi":"10.1515/ijb-2020-0073","title":"Doubly robust adaptive LASSO for effect modifier discovery","year":2022,"lang":"en","type":"article","venue":"The International Journal of Biostatistics","topic":"Advanced Causal Inference Techniques","field":"Mathematics","cited_by":15,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University; Université de Montréal","funders":"Canadian Institutes of Health Research","keywords":"Counterfactual thinking; Lasso (programming language); Propensity score matching; Outcome (game theory); Observational study; Covariate; Marginal structural model; Set (abstract data type); Conditional expectation","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":[0.00107925,0.0001260066,0.0002147359,0.0001112714,0.000136202,0.00007160407,0.0007775993,0.00002500286,0.00006056457],"category_scores_gemma":[0.001191758,0.00008586997,0.0001284242,0.00007201898,0.00007657733,0.0001947049,0.0002011412,0.0003354802,0.000001399132],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003011543,"about_ca_system_score_gemma":0.0001016993,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000007240218,"about_ca_topic_score_gemma":0.000004979801,"domain_scores_codex":[0.9984675,0.000116593,0.0004461903,0.00009463931,0.000730436,0.0001446347],"domain_scores_gemma":[0.9955086,0.003198523,0.0006496213,0.0001431546,0.0004643261,0.00003584484],"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":[0.002062084,0.0002376381,0.0001121099,0.0000241043,0.000605076,0.0001256667,0.0006786723,0.006260634,0.003261105,0.8992999,0.07899664,0.008336353],"study_design_scores_gemma":[0.001141507,0.001518629,0.00006231498,0.00005342593,0.0001356319,0.0003336166,0.0003735334,0.004620867,0.009064841,0.9760906,0.006414226,0.00019088],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02177048,0.00005188906,0.9745559,0.00121863,0.001013616,0.0003043018,0.0007045006,0.00002686437,0.0003538509],"genre_scores_gemma":[0.8545878,0.00001952589,0.1438229,0.0003555446,0.0003660491,0.00004952942,0.00001760635,0.0000338513,0.000747205],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.8328173,"threshold_uncertainty_score":0.3501676,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1331977124049428,"score_gpt":0.3993879005224699,"score_spread":0.2661901881175271,"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."}}