Performance Comparison of CausalMixFT with SMOTE and MixUp in Tabular Foundation Model Fine-Tuning

Abstract

Abstract: Fine-tuning tabular foundation models (TFMs) under data scarcity is challenging, as early stopping on even scarcer validation data often fails to capture true generalization performance. We propose CausalMixFT, a method that enhances fine-tuning robustness and downstream performance by generating structurally consistent synthetic samples using Structural Causal Models (SCMs) fitted on the target dataset. This approach augments limited real data with causally informed synthetic examples, preserving feature dependencies while expanding training diversity. Evaluated across 33 classification datas

Research Question

How does the performance of CausalMixFT compare to other data augmentation techniques like SMOTE or MixUp in fine-tuning tabular foundation models, as measured by validation accuracy and downstream task generalization on TabularGLUE benchmarks?

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