TabSCM: A practical Framework for Generating Realistic Tabular Data

TL;DR

TabSCM is a practical framework for generating realistic, causally consistent tabular data that outperforms existing methods in fidelity, utility, and privacy, while enabling efficient and interpretable counterfactual analysis.

Contribution

It introduces a causal structure-aware generator for tabular data that combines explicit equations, topological modeling, and diffusion models for improved realism and interpretability.

Findings

Matches or surpasses state-of-the-art in fidelity, utility, and privacy.

Runs up to 583 times faster than diffusion-only models.

Reduces rule-violation rates and supports causal interventions.

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.