Fighting Spurious Correlations in Text Classification via a Causal Learning Perspective

TL;DR

This paper introduces CCR, a causal learning-based method for text classification that reduces reliance on spurious correlations, thereby enhancing robustness and generalization, especially in out-of-distribution scenarios.

Contribution

The paper proposes a novel causal feature selection and weighting approach, improving robustness without requiring group labels, and provides theoretical and empirical validation.

Findings

CCR achieves state-of-the-art results without group labels.

CCR outperforms existing methods on robustness metrics.

In some cases, CCR rivals models with group labels.

Abstract

In text classification tasks, models often rely on spurious correlations for predictions, incorrectly associating irrelevant features with the target labels. This issue limits the robustness and generalization of models, especially when faced with out-of-distribution data where such spurious correlations no longer hold. To address this challenge, we propose the Causally Calibrated Robust Classifier (CCR), which aims to reduce models' reliance on spurious correlations and improve model robustness. Our approach integrates a causal feature selection method based on counterfactual reasoning, along with an unbiased inverse propensity weighting (IPW) loss function. By focusing on selecting causal features, we ensure that the model relies less on spurious features during prediction. We theoretically justify our approach and empirically show that CCR achieves state-of-the-art performance among methods without group labels, and in some cases, it can compete with the models that utilize group labels.