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The estimation of treatment effects is a pervasive problem in medicine.Existing methods for estimating treatment effects from longitudinalobservational data assume that there are no hidden confounders, an assumptionthat is not testable in practice and, if it does not hold, leads to biasedestimates. In this paper, we develop the Time Series Deconfounder, a methodthat leverages the assignment of multiple treatments over time to enable theestimation of treatment effects in the presence of multi-cause hiddenconfounders. The Time Series Deconfounder uses a novel recurrent neural networkarchitecture with multitask output to build a factor model over time and inferlatent variables that render the assigned treatments conditionally independent;then, it performs causal inference using these latent variables that act assubstitutes for the multi-cause unobserved confounders. We provide atheoretical analysis for obtaining unbiased causal effects of time-varyingexposures using the Time Series Deconfounder. Using both simulated and realdata we show the effectiveness of our method in deconfounding the estimation oftreatment responses over time.

Time Series Deconfounder: Estimating Treatment Effects over Time in the Presence of Hidden Confounders