CausalMixFT Synthetic-to-Real Data Ratio Effects on Tabular Foundation Model Fine-Tuning and OOD 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 ratio of synthetic to real data in CausalMixFT on the fine-tuning performance of tabular foundation models, evaluated using test accuracy and out-of-distribution generalization metrics?

Autonomous synthesis report generated by Assignee Research. Tribunal consensus score: 8.3/10.