Impact of Causal Augmentation Proportions on Tabular Foundation Model Robustness and Generalization

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: What is the impact of varying the proportion of causally augmented synthetic data in the fine-tuning process on the robustness and generalization performance of tabular foundation models, as measured by accuracy on the TabBench benchmark suite?

Autonomous synthesis report generated by Assignee Research. Tribunal consensus score: 8.1/10.