Do regularization methods for shortcut mitigation work as intended?

TL;DR

This paper analyzes whether regularization methods can effectively mitigate shortcut learning without erasing causal features. By framing the problem with known concepts, unknown concepts, and shortcuts in a linear (and extendable to nonlinear) setting, it derives theoretical conditions (Propositions 1 and 2) under which L1, L2, EYE, and causal regularization succeed or fail. The authors corroborate the theory with synthetic and real-world experiments (Colored-MNIST, MultiNLI, MIMIC-ICU), illustrating that mitigation depends heavily on correlations among shortcuts, known/unknown concepts, and outcomes, and that over-regularization is a real risk. The work highlights the necessity of accurate causal property estimation and dataset diversification, and calls for new methods that better disentangle causal signals from shortcuts to improve robustness under distribution shifts.

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.

Figures (9)

• Figure 1: Causal graph for shortcut learning. $Y\in\mathbb{R}$ is the output feature. $\bm{X}\in\mathbb{R}^d$ represents the input features. $\bm{C}\in\mathbb{R}^c$ denotes the known concepts. Unknown concepts ($\bm{U}\in\mathbb{R}^u$) also cause $Y$ but are not observed or known. Shortcuts ($\bm{S}\in\mathbb{R}^s$) that have spurious correlation with $Y$ can be extracted from $\bm{X}$. $\bm{U}$ and $\bm{S}$ are mixed together. Observed variables are in gray. Dashed/solid edges represent correlation that is broken/unaffected under distribution shifts.
• Figure 2: Comparison of trained weights and estimated treatment effects across training epochs. (A) Regularization are successful when $S$ is only correalted with $\bm{C}$. (B) Regularization fail when $S$ is correlated with $\bm{U}$. (C) Regularization fail when $S$ is highly correlated with $Y$. In the weight comparison plots, black bars represent the true weights assigned to each variable, consistent across different shortcut scenarios. The treatment effect plots depict the estimated treatment effect of the shortcut variable throughout the training epochs.
• Figure 3: Correlation between predicted values and all variables in synthetic experiments, including true outputs in the test dataset, under different regularization methods. As the correlation between outputs and unknown concepts increases, regularization methods become less effective in mitigating shortcut dependencies.
• Figure 4: Comparison of weights and test loss (AUC for classification problems and MSE for regression problem) across varying regularization strengths for synthetic dataset, Colored-MNIST, MultiNLI, and MIMIC-IV dataset. Experiments were conducted ten times; the solid line represents the mean results, while the shaded area indicates the standard error of the ten experiments.
• Figure 5: Treatment effect estimation of the shortcut variable along with training epoch when applying regularization terms to different shortcut types. Neural networks might undermine the effectiveness of regularization methods. (A) Shortcut $S$ is only correlated with $C_1$ and $C_2$. (B) $S$ is correlated with $U_1$ and $U_2$. (C) $S$ is correlated with $Y$.