# A Shift in Perspective on Causality in Domain Generalization

arXiv: https://arxiv.org/abs/2508.12798 · HTML: https://mlsanity.com/paper/2508.12798/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 1 of its 3 references that are in the map.
- First posted on arXiv 18 Aug 2025.

## Builds on (most cited first, 1 of 1)

- [Do causal predictors generalize better to new domains?](https://mlsanity.com/paper/2402.09891/) (2024)

## Built on by (most cited first, 1 of 1)

- [Information Theory in Open-world Machine Learning Foundations, Frameworks, and Future Direction](https://mlsanity.com/paper/2510.15422/) (2025)

## Abstract (arXiv)

The promise that causal modelling can lead to robust AI generalization has been challenged in recent work on domain generalization (DG) benchmarks. We revisit the claims of the causality and DG literature, reconciling apparent contradictions and advocating for a more nuanced theory of the role of causality in generalization. We also provide an interactive demo at https://chai-uk.github.io/ukairs25-causal-predictors/.