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

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
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
Series:Scientific Reports
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
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https://doi.org/10.1038/s41598-025-14460-8

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