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 CausalMixFT-generated synthetic data affect the fine-tuning convergence speed and validation accuracy of tabular foundation models when evaluated on standard benchmarks like TabPFN or TabMWP compared to traditional data augmentation methods?Autonomous synthesis report generated by Assignee Research. Tribunal consensus score: 7.7/10.