arXiv:2508.12798cs.LGcs.AI2025-08被引 1
重新审视因果在领域泛化中的作用，提出更精细的理论框架。
A Shift in Perspective on Causality in Domain Generalization
- 从因果视角重构领域泛化中的变量关系建模
- 揭示现有基准测试中因果性假设的局限性
- 适合关注模型鲁棒性与可解释性的研究者
近年来，因果建模能否实现稳健人工智能泛化的前景受到领域泛化（DG）基准测试的挑战。本文重新审视因果性与领域泛化文献中的主张，调和了看似矛盾的观点，并倡导对因果性在泛化中作用的更细致理论。我们还提供了交互式演示：https://chai-uk.github.io/ukairs25-causal-predictors/。
原文摘要 · Abstract (English)
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/.
因果推理领域泛化模型鲁棒性
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