Causal Forest Models with Double Machine Learning for Heterogeneous Treatment Effects in Antihypertensive Therapy: A Position Paper on Personalized Prescribing from Observational EHR Data

cirpublications.com-yBxZ-1790880679-e366460051 1790880679 Clinical Intelligence Research Press info@cirpublications.com Clinical Intelligence Research Press Journal of Artificial Intelligence for Healthcare Systems J. Artif. Intell. Healthc. Syst. 3149-8981 2024 3 1 Causal Forest Models with Double Machine Learning for Heterogeneous Treatment Effects in Antihypertensive Therapy: A Position Paper on Personalized Prescribing from Observational EHR Data Anna Kowalska Piotr Nowak Tomasz Zielinski Katarzyna Mazur 2024 10.68159/e366460051 https://cirpublications.com/pub/journal/1/article/e366460051 Wager S, Athey S. Estimation and inference of heterogeneous treatment effects using random forests. J Am Stat Assoc. 2018;113(523):1228-42. Chernozhukov V, Chetverikov D, Demirer M, Duflo E, Hansen C, Newey W, et al. Double/debiased machine learning for treatment and structural parameters. Econom J. 2018;21(1):C1-C68. Jaiswal JK, Samikannu R. 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