IdentifyingCausal-EffectInferenceFailurewith Uncertainty-AwareModels

This application is often needed in safety-critical domains suchashealthcare, whereestimating andcommunicating uncertainty to decision-makers iscrucial. Weintroduce apractical 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 highdimensional data, where standard applications of causal effect approaches fail.