Do regularization methods for shortcut mitigation work as intended?
This work addresses the challenge of improving model generalization by mitigating shortcuts, but it is incremental as it analyzes existing regularization techniques rather than proposing a new solution.
The paper tackles the problem of regularization methods inadvertently suppressing causal features while mitigating shortcuts in models, and finds that these methods can overregularize under certain conditions, with experiments on synthetic and real-world datasets providing insights into their limitations.
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.