aiqbench analysis
This research addresses a critical gap in synthetic data generation by demonstrating that statistical similarity does not inherently preserve causal effects, a vital requirement for robust downstream inference. The authors introduce a novel training framework that incorporates a causal discrepancy penalty directly into the generative objective to explicitly optimize for causal fidelity alongside standard distributional accuracy. By validating this approach on treatment-effect simulations and benchmark datasets, the work provides a significant advancement for machine learning practitioners relying on synthetic tabular data for scientific or policy analysis.
Beyond Distributional Fidelity: Causal-Penalized Diffusion for Synthetic Tabular Data
arXiv:2610.11407v1 Announce Type: cross Abstract: Synthetic tabular generators are commonly optimized for distributional fidelity, but statistical similarity alone does not guarantee preservation of causal effects. In this paper, we study whether causal fidelity can be improved directly within a fully generative tabular model. Causal Fidelity is defined with respect to a target estimand as the discrepancy between inferential distributions obtained from real and synthetic data, and theoretical results show that high statistical fidelity does not generally imply high causal fidelity. We then propose a causal-fidelity-aware training framework which adds a causal discrepancy penalty to the generative objective. The framework is instantiated with a causal-penalized TabDDPM and optimized using an on-policy score-function estimator. We further establish conditions under which causal regularization improves expected causal fidelity. Experiments across diverse treatment-effect simulations and two benchmark datasets evaluate the ability of our method to improve causal fidelity while preserving competitive statistical fidelity.
Abstract:Synthetic tabular generators are commonly optimized for distributional fidelity, but statistical similarity alone does not guarantee preservation of causal effects. In this paper, we study whether causal fidelity can be improved directly within a fully generative tabular model. Causal Fidelity is defined with respect to a target estimand as the discrepancy between inferential distributions obtained from real and synthetic data, and theoretical results show that high statistical fidelity does not generally imply high causal fidelity. We then propose a causal-fidelity-aware training framework which adds a causal discrepancy penalty to the generative objective. The framework is instantiated with a causal-penalized TabDDPM and optimized using an on-policy score-function estimator. We further establish conditions under which causal regularization improves expected causal fidelity. Experiments across diverse treatment-effect simulations and two benchmark datasets evaluate the ability of our method to improve causal fidelity while preserving competitive statistical fidelity.
Machine Learning (stat.ML); Machine Learning (cs.LG)
arXiv:2610.11407 [stat.ML]
(or arXiv:2610.11407v1 [stat.ML] for this version)
https://doi.org/10.48550/arXiv.2610.11407 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
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