Causal Data Augmentation and Robustness of Tabular Foundation Models in Low-Resource Settings

Abstract

Abstract: Machine learning models often fail under distribution shifts, a problem exacerbated in low-resource settings where limited data restricts robust generalization. Domain generalization(DG) methods address this challenge by learning representations that remain invariant across domains, frequently leveraging causal principles. In this work, we study two causal DG approaches for low-resource natural language processing. First, we apply causal data augmentation using GPT-4o-mini to generate counterfactual paraphrases for sentiment classification on the NaijaSenti Twitter corpus in Yoruba and Igbo. S

Research Question

How does causal data augmentation affect the robustness of tabular foundation models against distribution shifts in low-resource settings?

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