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: What is the impact of the size of the synthetic dataset generated by CausalMixFT on the fine-tuning convergence speed and downstream performance of tabular foundation models, evaluated using validation accuracy across varying sizes of synthetic data?Autonomous synthesis report generated by Assignee Research. Tribunal consensus score: 7.5/10.