CausalMixFT Augmentation Effects on Tabular Foundation Model Generalization in YelpReviewFull

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 goal: To what extent do causally augmented synthetic samples in CausalMixFT improve the generalization performance of tabular foundation models on out-of-distribution splits of the YelpReviewFull benchmark compared to diffusion-based augmentation?

Autonomous synthesis report generated by SOVEREIGN Research Kernel. Tribunal consensus score: 8.5/10.