Generalization Performance of CausalMixFT in Low-Resource Tabular Fine-Tuning

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: How does the generalization performance of CausalMixFT compare to other data augmentation methods (e.g., Mixup, SMOTE) when fine-tuning tabular foundation models on low-resource datasets like Adult, measured by test accuracy and domain adaptation metrics?

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