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arXiv Machine Learning By Lan Tao, Yongxian He, Shirong Xu, Yidong Ouyang, Guang Cheng

Beyond Distributional Fidelity: Causal-Penalized Diffusion for Synthetic Tabular Data

Read the original on arXiv Machine Learning →

The paper introduces a causal‑penalized diffusion model for generating synthetic tabular data, arguing that high statistical fidelity does not guarantee accurate causal inference. It defines causal fidelity as the discrepancy between inferential distributions from real and synthetic data and proposes a training framework that adds a causal discrepancy penalty to the generative objective. Experiments on treatment‑effect simulations and benchmark datasets demonstrate that the method can improve causal fidelity while maintaining competitive statistical fidelity.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Machine Learning.

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