Anti-causal domain generalization: Leveraging unlabeled data

One-line Summary

The paper proposes methods for domain generalization using unlabeled data by focusing on anti-causal settings where outcomes cause covariates, allowing for model robustness against distribution shifts.

Plain-language Overview

This research addresses the challenge of building models that work well in new environments, even when there's a shift in data distributions. Traditional methods need labeled data from different environments, which isn't always available. The authors explore a situation where the result influences the data we observe, not the other way around. This unique perspective allows them to use unlabeled data to make models less sensitive to changes in the data environment. They introduce two techniques to achieve this and show that their approach works well on both a simulated physical system and real-world physiological data.

Technical Details