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.

翻译：领域泛化（Domain Generalization，DG）旨在学习能够泛化到与训练域相关但有所不同的新域的模型。这是机器学习中的一个基本问题，近年来引起了广泛关注。目前已有大量方法被提出，这些方法源于不同视角，使得难以对该领域形成整体理解。本文提出了一个用于领域泛化的因果框架，并在该框架下对常见的DG方法进行了解读。我们的工作为以下问题提供了新见解：(1) 每种DG方法背后的关键思想是什么？(2) 为何理论上预期这些方法能提升对新域的泛化能力？(3) 不同DG方法之间如何相互关联，各自的相对优势与局限性是什么？通过提供领域泛化的统一视角，我们期望帮助研究者更好地理解其基本原理，并为这一机器学习中的关键问题开发更有效的方法。