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Estimating treatment effects, especially individualized treatment effects(ITE), using observational data is challenging due to the complex situations ofconfounding bias. Existing approaches for estimating treatment effects fromlongitudinal observational data are usually built upon a strong assumption of"unconfoundedness", which is hard to fulfill in real-world practice. In thispaper, we propose the Variational Temporal Deconfounder (VTD), an approach thatleverages deep variational embeddings in the longitudinal setting using proxies(i.e., surrogate variables that serve for unobservable variables).Specifically, VTD leverages observed proxies to learn a hidden embedding thatreflects the true hidden confounders in the observational data. As such, ourVTD method does not rely on the "unconfoundedness" assumption. We test our VTDmethod on both synthetic and real-world clinical data, and the results showthat our approach is effective when hidden confounding is the leading biascompared to other existing models.

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