mlsanityData to 14 Oct 2026

A Causal Framework to Unify Common Domain Generalization Approaches

arXiv 2307.06825 ↗First posted 13 Jul 2023cs.LG cs.AIcited by 3

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Abstract (arXiv)

Domain generalization (DG) is about learning models that generalize well to new domains that are related to, but different from, the training domain(s). It is a fundamental problem in machine learning and has attracted much attention in recent years. A large number of approaches have been proposed. Different approaches are motivated from different perspectives, making it difficult to gain an overall understanding of the area. In this paper, we propose a causal framework for domain generalization and present an understanding of common DG approaches in the framework. Our work sheds new lights on the following questions: (1) What are the key ideas behind each DG method? (2) Why is it expected to improve generalization to new domains theoretically? (3) How are different DG methods related to each other and what are relative advantages and limitations? By providing a unified perspective on DG, we hope to help researchers better understand the underlying principles and develop more effective approaches for this critical problem in machine learning.

Key facts. Cited by 3 papers in the map (data through 14 Oct 2026). Builds on 49 of its 62 references that are in the map. First posted on arXiv 13 Jul 2023.

Builds on

49 of 62 references.

References · 49

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37 more
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Cited by year

Citing papers per year: 2024: 1; 2025: 2.

3 papers.

  1. 2024Dual Risk Minimization: Towards Next-Level Robustness in Fine-tuning Zero-Shot Modelscited by 2
  2. 2025Why Domain Generalization Fail? A View of Necessity and Sufficiencycited by 2
  3. 2025Moment Alignment: Unifying Gradient and Hessian Matching for Domain Generalizationcited by 2