2023
DOI: 10.21203/rs.3.rs-2536079/v1
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Variational Temporal Deconfounder for Individualized Treatment Effect Estimation with Longitudinal Observational Data

Abstract: 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 variables). Methods We build VTD by incorporating a variational recurrent autoencoder that learns the latent encodings of hidden confounders from observed proxies and an ITE estimation network that takes the learned h… Show more

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References 27 publications
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