AI RESEARCH
DAGAF: A directed acyclic generative adversarial framework for joint structure learning and tabular data synthesis
arXiv CS.LG
•
ArXi:2604.04290v1 Announce Type: new 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