TabSCM: A practical Framework for Generating Realistic Tabular Data
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 se...
| Authors | Sven Jacob, Bardh Prenkaj, Weijia Shao et al. |
|---|---|
| Primary category | cs.LG |
| Published | 24 April 2026 |
| Last updated at source | 24 April 2026 |
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