AI RESEARCH
Improving TabPFN's Synthetic Data Generation by Integrating Causal Structure
arXiv CS.LG
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ArXi:2603.10254v1 Announce Type: new 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.