Causal-Aware Tabular GANs with Reinforcement Learning

Excerpt

arXiv:2510.24046v2 Announce Type: replace Abstract: Existing tabular data generation methods primarily focus on matching statistical distributions between real and synthetic data, often overlooking the preservation of underlying causal relationships. As a result, generated samples may appear realistic while failing to maintain the causal structure required for reliable downstream analysis. We propose CA-GAN, a causal-aware generative framework for tabular data synthesis that explicitly incorpora