Structural Causal Model Fidelity in Data Augmentation for Few-Shot Tabular Foundation Models

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 fidelity of structural causal models used for data augmentation impact the few-shot classification accuracy of fine-tuned tabular foundation models compared to standard generative augmentation?

Autonomous synthesis report generated by Assignee Research. Tribunal consensus score: 8.7/10.