  Using observational data to estimate the effect of a treatment is a powerful
tool for decision-making when randomized experiments are infeasible or costly.
However, observational data often yields biased estimates of treatment effects,
since treatment assignment can be confounded by unobserved variables. A remedy
is offered by deconfounding methods that adjust for such unobserved
confounders. In this paper, we develop the Sequential Deconfounder, a method
that enables estimating individualized treatment effects over time in presence
of unobserved confounders. This is the first deconfounding method that can be
used in a general sequential setting (i.e., with one or more treatments
assigned at each timestep). The Sequential Deconfounder uses a novel Gaussian
process latent variable model to infer substitutes for the unobserved
confounders, which are then used in conjunction with an outcome model to
estimate treatment effects over time. We prove that using our method yields
unbiased estimates of individualized treatment responses over time. Using
simulated and real medical data, we demonstrate the efficacy of our method in
deconfounding the estimation of treatment responses over time.
