{"id":"W1996144317","doi":"10.1515/jci-2014-0022","title":"A Boosting Algorithm for Estimating Generalized Propensity Scores with Continuous Treatments","year":2014,"lang":"en","type":"article","venue":"Journal of Causal Inference","topic":"Advanced Causal Inference Techniques","field":"Mathematics","cited_by":153,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"Eunice Kennedy Shriver National Institute of Child Health and Human Development; National Institute of Diabetes and Digestive and Kidney Diseases; National Institute of Child Health and Human Development; National Institute on Drug Abuse; National Institutes of Health","keywords":"Covariate; Mathematics; Propensity score matching; Statistics; Boosting (machine learning); Causal inference; Estimator; Average treatment effect; Nonparametric statistics; Curse of dimensionality; Weighting; Econometrics; Algorithm; Computer science; Machine learning; Medicine","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":[],"consensus_categories":[],"category_scores_codex":[0.01363919,0.00134535,0.003590624,0.00219822,0.001089594,0.001298607,0.003152578,0.002038579,0.004099896],"category_scores_gemma":[0.02545111,0.001437039,0.002450947,0.002724034,0.001196014,0.001899439,0.002412052,0.003350067,0.001578952],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009872231,"about_ca_system_score_gemma":0.002605663,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002884881,"about_ca_topic_score_gemma":0.002446387,"domain_scores_codex":[0.9938116,0.00428676,0.0002468317,0.000670091,0.0007480054,0.000236665],"domain_scores_gemma":[0.9912982,0.006253347,0.0005037409,0.0007817083,0.0009300921,0.0002328874],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.0002931386,0.0001799996,0.003835142,0.0003353096,0.0004475964,0.0001730324,0.0001990895,0.5200761,0.001717641,0.08204456,0.005571033,0.3851274],"study_design_scores_gemma":[0.0001037168,0.00007612171,0.0003660598,0.00003055953,0.00004768238,0.00006002618,0.00001129194,0.9363454,0.0003680117,0.06014909,0.002422324,0.00001982382],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.001293917,0.0001471453,0.9980738,0.00006663177,0.00002496662,0.00005449343,0.00002675329,0.0001683836,0.0001438385],"genre_scores_gemma":[0.06381825,0.0003765261,0.9332635,0.0002318334,0.0001425246,0.0006416026,0.0003599318,0.0001214102,0.001044419],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.01363919,"threshold_uncertainty_score":0.07213181,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1391485150001477,"score_gpt":0.4045267184493015,"score_spread":0.2653782034491539,"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."}}