Contrastive-ACE: Domain Generalization Through Alignment of Causal Mechanisms

Area of Science:

• Machine Learning
• Artificial Intelligence
• Causal Inference

Background:

• Domain generalization seeks invariant knowledge across data distributions for improved performance on unseen domains.
• Existing methods often leverage feature invariance, but this study explores the invariance of causal effects.
• Causality is closely linked to invariance, providing a foundation for robust generalization.

Purpose of the Study:

• To develop a domain generalization method that enforces the invariance of the average causal effect (ACE) of features on labels.
• To improve model generalization by regularizing training through feature interventions.
• To introduce the invariance of causal mechanisms into the machine learning process.

Main Methods:

• The proposed method regularizes training by performing interventions on features.
• This enforces the stability of causal predictions made by the classifier across different domains.
• The core idea is to ensure the average causal effect remains invariant.

Main Results:

• Experiments on benchmark datasets show the proposed method outperforms state-of-the-art approaches.
• The approach demonstrates improved domain generalization capabilities.
• The effectiveness of enforcing ACE invariance is validated.

Conclusions:

• The study highlights the importance of causal mechanism invariance for domain generalization.
• The proposed method offers a novel perspective by focusing on the stability of causal effects.
• This research contributes to understanding and improving generalization in machine learning.