  The estimation of treatment effects is a pervasive problem in medicine.
Existing methods for estimating treatment effects from longitudinal
observational data assume that there are no hidden confounders, an assumption
that is not testable in practice and, if it does not hold, leads to biased
estimates. In this paper, we develop the Time Series Deconfounder, a method
that leverages the assignment of multiple treatments over time to enable the
estimation of treatment effects in the presence of multi-cause hidden
confounders. The Time Series Deconfounder uses a novel recurrent neural network
architecture with multitask output to build a factor model over time and infer
latent variables that render the assigned treatments conditionally independent;
then, it performs causal inference using these latent variables that act as
substitutes for the multi-cause unobserved confounders. We provide a
theoretical analysis for obtaining unbiased causal effects of time-varying
exposures using the Time Series Deconfounder. Using both simulated and real
data we show the effectiveness of our method in deconfounding the estimation of
treatment responses over time.
