What is the impact of varying levels of class skew in synthetic data augmentation on the F1-score stability of

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

What is the impact of varying levels of class skew in synthetic data augmentation on the F1-score stability of tabular foundation models (TFMs) when fine-tuned and evaluated on the UCI ML datasets (e.g., Breast Cancer, Heart Disease) compared to the original real data?

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