  Estimating the individual treatment effect (ITE) from observational data is
meaningful and practical in healthcare. Existing work mainly relies on the
strong ignorability assumption that no hidden confounders exist, which may lead
to bias in estimating causal effects. Some studies consider the hidden
confounders are designed for static environment and not easily adaptable to a
dynamic setting. In fact, most observational data (e.g., electronic medical
records) is naturally dynamic and consists of sequential information. In this
paper, we propose Deep Sequential Weighting (DSW) for estimating ITE with
time-varying confounders. Specifically, DSW infers the hidden confounders by
incorporating the current treatment assignments and historical information
using a deep recurrent weighting neural network. The learned representations of
hidden confounders combined with current observed data are leveraged for
potential outcome and treatment predictions. We compute the time-varying
inverse probabilities of treatment for re-weighting the population. We conduct
comprehensive comparison experiments on fully-synthetic, semi-synthetic and
real-world datasets to evaluate the performance of our model and baselines.
Results demonstrate that our model can generate unbiased and accurate treatment
effect by conditioning both time-varying observed and hidden confounders,
paving the way for personalized medicine.
