Variational Temporal Deconfounder for Individualized Treatment Effect Estimation from Longitudinal Observational Data
This work addresses the problem of confounding bias in treatment effect estimation for fields like healthcare, offering a method that relaxes a strong assumption but is incremental in its approach.
The paper tackles the challenge of estimating individualized treatment effects from longitudinal observational data by proposing the Variational Temporal Deconfounder (VTD), which uses deep variational embeddings with proxies to address hidden confounding without relying on the unconfoundedness assumption, and shows effectiveness on synthetic and real-world clinical data when hidden confounding is the main bias.
Estimating treatment effects, especially individualized treatment effects (ITE), using observational data is challenging due to the complex situations of confounding bias. Existing approaches for estimating treatment effects from longitudinal observational data are usually built upon a strong assumption of "unconfoundedness", which is hard to fulfill in real-world practice. In this paper, we propose the Variational Temporal Deconfounder (VTD), an approach that leverages 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 that reflects the true hidden confounders in the observational data. As such, our VTD method does not rely on the "unconfoundedness" assumption. We test our VTD method on both synthetic and real-world clinical data, and the results show that our approach is effective when hidden confounding is the leading bias compared to other existing models.