Application of causal forest double machine learning (DML) approach to assess tuberculosis preventive therapy’s impact on ART adherence

Abstract Adherence to antiretroviral therapy (ART) is critical for HIV treatment success, yet the impact of tuberculosis preventive therapy (TPT) remains inadequately understood. Using observational data from 4152 HIV patients in Ethiopia (2005–2024), we applied causal inference methods, including A...

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Bibliographic Details

Published in:	Scientific Reports
Main Authors:	Abraham Keffale Mengistu, Kelemua Aschale Yeneakale, Nebebe Demis Baykemagn, Zelalem Yitayal Melese, Andualem Enyew Gedefaw
Format:	Article
Language:	English
Published:	Nature Portfolio 2025-08-01
Subjects:	Causal machine learning Double machine learning Tuberculosis preventive therapy ART adherence HIV Heterogeneous treatment effects
Online Access:	https://doi.org/10.1038/s41598-025-14460-8