TabSCM: A practical Framework for Generating Realistic Tabular Data

• URL: http://arxiv.org/abs/2604.22337v1
• Date: Fri, 24 Apr 2026 08:10:01 GMT
• Title: TabSCM: A practical Framework for Generating Realistic Tabular Data
• Abstract summary: We present TabSCM, a mixed-type generator that preserves causal dependencies.<n>On seven public datasets, TabSCM matches or surpasses state-of-the-art GAN, diffusion, and LLM baselines in statistical fidelity.<n>Because generation is decomposed into explicit equations, it runs up to 583$times$ faster than diffusion-only models.
• Score: 22.41489215805826
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Most tabular-data generators match marginal statistics yet ignore causal structure, leading downstream models to learn spurious or unfair patterns. We present TabSCM, a mixed-type generator that preserves those causal dependencies. Starting from a Completed Partially Directed Acyclic Graph (CPDAG) found by any causal structure discovery algorithm, TabSCM (i) orients edges to a DAG, (ii) fits root-node marginals with KDE or categorical frequencies, and (iii) learns topologically ordered structural assignments. Such assignments are achieved using conditional diffusion models for continuous variables as child nodes and gradient-boosted trees for categorical ones. Ancestral sampling yields semantically valid records and enables exact counterfactual queries. On seven public datasets, encompassing healthcare, finance, housing, environment, TabSCM matches or surpasses state-of-the-art GAN, diffusion, and LLM baselines in statistical fidelity, downstream utility, and privacy risk, while also cutting rule-violation rates and providing causally meaningful and robust conditional interventions. Because generation is decomposed into explicit equations, it runs up to 583$\times$ faster than diffusion-only models and exposes interpretable knobs for fairness auditing and policy simulation, making TabSCM a practical choice for realism, explainability, and causal soundness.

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