  Recommending the best course of action for an individual is a major
application of individual-level causal effect estimation. This application is
often needed in safety-critical domains such as healthcare, where estimating
and communicating uncertainty to decision-makers is crucial. We introduce a
practical approach for integrating uncertainty estimation into a class of
state-of-the-art neural network methods used for individual-level causal
estimates. We show that our methods enable us to deal gracefully with
situations of "no-overlap", common in high-dimensional data, where standard
applications of causal effect approaches fail. Further, our methods allow us to
handle covariate shift, where test distribution differs to train distribution,
common when systems are deployed in practice. We show that when such a
covariate shift occurs, correctly modeling uncertainty can keep us from giving
overconfident and potentially harmful recommendations. We demonstrate our
methodology with a range of state-of-the-art models. Under both covariate shift
and lack of overlap, our uncertainty-equipped methods can alert decisions
makers when predictions are not to be trusted while outperforming their
uncertainty-oblivious counterparts.
