Title: Cause-Effect Deep Information Bottleneck For Incomplete Covariates,
Abstract: Estimating the causal effects of an intervention in the presence of
confounding is a frequently occurring problem in applications such as medicine.
The task is challenging since there may be multiple confounding factors, some
of which may be missing, and inferences must be made from high-dimensional,
noisy measurements. In this paper, we propose a decision-theoretic approach to
estimate the causal effects of interventions where a subset of the covariates
is unavailable for some patients during testing. Our approach uses the
information bottleneck principle to perform a discrete, low-dimensional
sufficient reduction of the covariate data to estimate a distribution over
confounders. In doing so, we can estimate the causal effect of an intervention
where only partial covariate information is available. Our results on a causal
inference benchmark and a real application for treating sepsis show that our
method achieves state-of-the-art performance, without sacrificing
interpretability.