Identifying Causal-Effect Inference Failure with Uncertainty-Aware Models
This work addresses the need for robust uncertainty communication in individual-level causal effect estimation, particularly in high-dimensional and shifting data environments, though it is incremental as it builds on existing neural network approaches.
The paper tackles the problem of unreliable causal-effect inference in safety-critical domains like healthcare by integrating uncertainty estimation into neural network methods, showing that these methods outperform uncertainty-oblivious counterparts in handling no-overlap and covariate shift scenarios.
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.