How does incorporating causal invariance in pre-training affect the out-of-distribution robustness of tabular

Abstract

Abstract: The performance of machine learning models under distribution shift has been the focus of the community in recent years. Most of current methods have been proposed to improve the robustness to distribution shift from the algorithmic perspective, i.e., designing better training algorithms to help the generalization in shifted test distributions. This paper studies the distribution shift problem from the perspective of pre-training and data augmentation, two important factors in the practice of deep learning that have not been systematically investigated by existing work. By evaluating seven pre

Research Question

How does incorporating causal invariance in pre-training affect the out-of-distribution robustness of tabular foundation models compared to non-causal baselines, measured via accuracy degradation on shifted synthetic datasets?

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