Variational Temporal Deconfounder for Individualized Treatment Effect Estimation with Longitudinal Observational Data

PURPOSE: This paper proposes a new approach, Variational Temporal Deconfounder (VTD), for estimating individualized treatment effects (ITE) from longitudinal observational data, where we address the hidden confounding issues by using proxies (i.e., surrogate variables that serve for unobservable var...

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Detalles Bibliográficos
Autores principales: Feng, Zheng, Prosperi, Mattia, Guo, Yi, Bian, Jiang
Formato: Online Artículo Texto
Lenguaje:English
Publicado: American Journal Experts 2023
Materias:
Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9934764/
https://www.ncbi.nlm.nih.gov/pubmed/36798248
http://dx.doi.org/10.21203/rs.3.rs-2536079/v1

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9934764/
https://www.ncbi.nlm.nih.gov/pubmed/36798248
http://dx.doi.org/10.21203/rs.3.rs-2536079/v1

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