Identifying Causal-Effect Inference Failure with Uncertainty-Aware Models

TL;DR

This paper presents a method to incorporate uncertainty estimation into neural network models for individual causal effect inference, improving safety and reliability in high-stakes domains like healthcare.

Contribution

It introduces a practical approach to integrate uncertainty into state-of-the-art causal inference models, handling issues like no-overlap and covariate shift effectively.

Findings

Uncertainty-aware models outperform traditional methods under covariate shift.

Models can identify when predictions are unreliable, aiding decision-making.

Handling no-overlap improves causal effect estimation in high-dimensional data.

Abstract

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.