Causal Structure Integration in Synthetic Data Generation for TabPFN Robustness

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: Does integrating causal structure into synthetic data generation improve the robustness of TabPFN against feature permutation compared to standard autoregressive training?

Autonomous synthesis report generated by Assignee Research. Tribunal consensus score: 8.8/10.