Comparative Performance of TabPFN, CTGAN, and CausalMixFT in Cross-Domain Tabular Data Generation

Abstract

Abstract: 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 Question

How do TabPFN, CTGAN, and CausalMixFT perform in cross-domain tabular data generation tasks when evaluated on both synthetic and real-world datasets?

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A claim in this record was tested against Gate 2 and failed: a counterexample was found, a proof did not type-check, or a reproduction attempt did not match the reported results. Evidence for the failure is attached to this record. VERIFIED requires an attached reproducible artifact (Lean4 proof source, or repro script and results) before this status can be set; it is not derived from review score or claim count.

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Quality Dimensions

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Public corrections are additive records. Current status does not claim the synthesis is error-free.

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