Fighting Spurious Correlations in Text Classification via a Causal Learning Perspective
This addresses robustness issues in text classification for AI applications, but it is incremental as it builds on existing causal methods.
The paper tackles the problem of models relying on spurious correlations in text classification, which limits robustness and generalization, by proposing the Causally Calibrated Robust Classifier (CCR) that integrates causal feature selection and an unbiased loss function, achieving state-of-the-art performance among methods without group labels.
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.