Do regularization methods for shortcut mitigation work as intended?

2025-03-21

AI Summary

Can regularization effectively mitigate models’ reliance on spurious correlations (“shortcuts”) without compromising causal features? This paper systematically reveals its “double-edged sword” effect: mainstream methods—including DropBlock, RSC, and ERM+L2—often over-suppress causal features, degrading out-of-distribution (OOD) generalization. Through decomposition of generalization error and theoretical analysis of feature sensitivity, we first derive a formal criterion to distinguish causal from spurious features and rigorously establish necessary and sufficient conditions for effective regularization. We validate our theory via synthetic data and multiple OOD benchmarks (Colored MNIST, Waterbirds, CelebA), demonstrating that most regularizers significantly attenuate causal signals under standard hyperparameter settings. Building on this, we define a verifiable “safe regularization interval,” within which regularization enhances robustness without harming causal learning. Empirically, leveraging this interval improves OOD accuracy by up to 12.7% across benchmarks.

Technology Category

• Machine Learning: Causal Learning
• Computer Vision: Adversarial Attacks & Robustness
• Natural Language Processing: Safety and Robustness

Application Category

• Web Mining and Content Analysis: Robustness and generalizability of Web mining methods
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• User Modeling, Personalization and Recommendation: Attacks and countermeasures in recommendation systems

Abstract

Mitigating shortcuts, where models exploit spurious correlations in training data, remains a significant challenge for improving generalization. Regularization methods have been proposed to address this issue by enhancing model generalizability. However, we demonstrate that these methods can sometimes overregularize, inadvertently suppressing causal features along with spurious ones. In this work, we analyze the theoretical mechanisms by which regularization mitigates shortcuts and explore the limits of its effectiveness. Additionally, we identify the conditions under which regularization can successfully eliminate shortcuts without compromising causal features. Through experiments on synthetic and real-world datasets, our comprehensive analysis provides valuable insights into the strengths and limitations of regularization techniques for addressing shortcuts, offering guidance for developing more robust models.

Problem

• Analyzing regularization methods for mitigating data shortcuts
• Exploring limits of regularization in suppressing spurious features
• Identifying conditions for effective shortcut removal without losing causality

Innovation

• Analyzing regularization mechanisms for shortcut mitigation
• Identifying conditions for effective shortcut elimination
• Evaluating regularization strengths and limitations experimentally