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 TruthfulQA benchmark performance of tabular foundation models compare when fine-tuned with CausalMixFT-generated synthetic data versus non-causal data augmentation techniques like SMOTE or GANs in low-data regimes?Autonomous synthesis report generated by Assignee Research. Tribunal consensus score: 7.7/10.