mlsanityData to 14 Oct 2026

A Shift in Perspective on Causality in Domain Generalization

arXiv 2508.12798 ↗First posted 18 Aug 2025cs.LG cs.AI cs.CVcited by 1

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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/.

Key facts. Cited by 1 paper 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.

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References · 1

  1. 2024Do causal predictors generalize better to new domains?cited by 11

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Citing papers per year: 2025: 1.

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  1. 2025Information Theory in Open-world Machine Learning Foundations, Frameworks, and Future Directioncited by 0