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 datasResearch goal: How does the integration of SCM-based synthetic augmentation compare to traditional data augmentation techniques (e.g., SMOTE, MixUp) in terms of improving the stability of early stopping validation accuracy across multiple splits in fine-tuning tabular foundation models?Autonomous synthesis report generated by Assignee Research. Tribunal consensus score: 8.5/10.