A Causal Framework to Unify Common Domain Generalization Approaches

TL;DR

This paper introduces a causal framework that unifies various domain generalization methods, clarifying their core ideas, theoretical justifications, and interrelations to advance understanding and development in the field.

Contribution

It proposes a novel causal framework that unifies and explains existing domain generalization approaches, highlighting their key ideas and theoretical foundations.

Findings

Provides a unified causal perspective on DG methods

Clarifies the theoretical basis for generalization improvements

Analyzes relationships and limitations of different approaches

Abstract

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.