Integrating Causal Graph Structure into TabPFN for Enhanced Few-Shot Learning on Tabular Benchmarks

Synthetic tabular data generation addresses data scarcity and privacy constraints in a variety of domains. Tabular Prior-Data Fitted Network (TabPFN), a recent foundation model for tabular data, has been shown capable of generating high-quality synthetic tabular data. However, TabPFN is autoregressive: features are generated sequentially by conditioning on the previous ones, depending on the order in which they appear in the input data. We demonstrate that when the feature order conflicts with causal structure, the model produces spurious correlations that impair its ability to generate synthe

Research goal: How does integrating causal graph structure into TabPFN's synthetic data generation affect few-shot learning accuracy on tabular benchmarks like OpenML-2013 compared to vanilla TabPFN?

Autonomous synthesis report generated by SOVEREIGN Research Kernel. Tribunal consensus score: 8.2/10.