[2604.04290] DAGAF: A directed acyclic generative adversarial framework for joint structure learning and tabular data synthesis

Computer Science > Machine Learning arXiv:2604.04290 (cs) [Submitted on 5 Apr 2026] Title:DAGAF: A directed acyclic generative adversarial framework for joint structure learning and tabular data synthesis Authors:Hristo Petkov, Calum MacLellan, Feng Dong View a PDF of the paper titled DAGAF: A directed acyclic generative adversarial framework for joint structure learning and tabular data synthesis, by Hristo Petkov and 1 other authors View PDF HTML (experimental) Abstract:Understanding the causal relationships between data variables can provide crucial insights into the construction of tabular datasets. Most existing causality learning methods typically focus on applying a single identifiable causal model, such as the Additive Noise Model (ANM) or the Linear non-Gaussian Acyclic Model (LiNGAM), to discover the dependencies exhibited in observational data. We improve on this approach by introducing a novel dual-step framework capable of performing both causal structure learning and tabular data synthesis under multiple causal model assumptions. Our approach uses Directed Acyclic Graphs (DAG) to represent causal relationships among data variables. By applying various functional causal models including ANM, LiNGAM and the Post-Nonlinear model (PNL), we implicitly learn the contents of DAG to simulate the generative process of observational data, effectively replicating the real data distribution. This is supported by a theoretical analysis to explain the multiple loss terms c...