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This work introduces a step-by-step token extraction procedure to extract causal shortcuts from data, and applies parallel prioritized masking on these tokens during training to enable efficient and accurate convergence to correct answers via causal shortcuts.
StablePFN is proposed, a novel tabular foundation model that integrates explicit causal awareness with stable predictive modeling and significantly outperforms state-of-the-art baselines in cross-environment prediction settings, particularly in challenging high-bias scenarios.
Pre-trained tabular prediction models based on Prior-Data Fitted Networks (PFNs), such as TabPFN and LimiX, have achieved remarkable progress in supervised learning, demonstrating immense potential across real-world scenarios and diverse downstream tasks. However, a critical question remains systematically unexplored:...
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