Causally motivated Shortcut Removal Using Auxiliary Labels

Abstract: Shortcut learning, in which models make use of easy-to-represent but unstable associations, is a major failure mode for robust machine learning. We study a flexible, causally-motivated approach to training robust predictors by discouraging the use of specific shortcuts, focusing on a common setting where a robust predictor could achieve optimal \emph{iid} generalization in principle, but is overshadowed by a shortcut predictor in practice. Our approach uses auxiliary labels, typically available at training time, to enforce conditional independences implied by the causal graph. We show both theoretically and empirically that causally-motivated regularization schemes (a) lead to more robust estimators that generalize well under distribution shift, and (b) have better finite sample efficiency compared to usual regularization schemes, even when no shortcut is present. Our analysis highlights important theoretical properties of training techniques commonly used in the causal inference, fairness, and disentanglement literatures. Our code is available at https://github.com/mymakar/causally_motivated_shortcut_removal

• The paper presents a novel approach that integrates causal inference with auxiliary labels to systematically discourage shortcut learning in deep neural networks.
• Using importance weighting and MMD-based regularization, the method enforces independent latent representations that reduce errors under distribution shifts.
• Empirical results demonstrate improved generalization and fairness in image classification and medical imaging tasks compared to conventional training methods.

Causally Motivated Shortcut Removal Using Auxiliary Labels

The paper "Causally Motivated Shortcut Removal Using Auxiliary Labels" presents a robust approach to mitigate the phenomenon of shortcut learning within machine learning models using auxiliary labels. Shortcut learning refers to the tendency of models to rely on unstable and often spurious correlations in the data, which can deteriorate predictive performance under distribution shifts. This paper explores methods to improve model robustness by systematically discouraging these shortcuts through causal inference techniques.

Introduction and Problem Context

Deep Neural Networks (DNNs) have been successful in numerous applications but often fail to maintain robustness under distribution shifts, particularly those that occur naturally. Shortcut learning is identified as a principal contributor to this brittleness, where models exploit easily representable features that correlate to the main label in training data but change across environments. Commonly, these shortcuts emerge due to correlations between features — for instance, between an image's foreground and background. Though the foreground object alone may be sufficient for accurate predictions, models might leverage background cues as shortcuts in their decision-making.

Approach and Methodology

The authors propose a causally-motivated training strategy using auxiliary labels available during training to enforce conditional independencies derived from a causal graph. The method employs two main components:

1. Importance Weighting: To recover the "unconfounded" distribution by re-weighting data points from the source distribution. This step aims to mimic a distribution where the auxiliary label (denoting shortcut features) is independent of the main label.
2. Causally-Motivated Regularization: The approach connects to an optimal risk invariant predictor by penalizing statistical dependence between the model’s latent representations and the auxiliary label, using Maximum Mean Discrepancy (MMD) within a predefined independence criterion.

Their method optimizes model parameters to minimize empirical risk while simultaneously enforcing the independence regularization of the latent space from the auxiliary label.

Theoretical Contributions

The paper provides theoretical backing, asserting that models trained using their approach:

• Achieve lower generalization error across varied distribution shifts compared to conventional models.
• Exhibit better finite sample efficiency warranted by their causally-motivated regularization.

Key propositions elucidate the theoretical basis for why penalizing shortcuts using the causal graph enhances model accuracy and reduces generalization error. These insights are drawn from the decomposition of risk invariance and relate to complexity measures such as Rademacher complexity and Gaussian process theory.

Empirical Validation

Experiments conducted within the domains of image classification involve semi-synthetic datasets and medical imaging tasks, specifically evaluating robustness in predicting pneumonia from chest X-rays. Their models significantly outperform baselines across distribution shifts by focusing on invariant predictors that de-emphasize shortcut learning.

Practical Implications

This research extends beyond shortcut learning, highlighting implications for broader machine learning problems, including fairness and causality, where adopting invariant learning paradigms ensures both fairness through independence and resilience against distribution perturbations.

The grounding in causal theory positions this work as a compelling argument for the systematic inclusion of causal tools in machine learning to anticipate and mitigate issues arising from distribution shifts.

Conclusion

The paper introduces an innovative application of causal methods to improve ML model generalization through the removal of shortcuts. It opens potential avenues for future work in operationalizing similar frameworks across diverse applications in AI, particularly within domains where distributional robustness is paramount to model reliability.