Causal-Aware Tabular GANs with Reinforcement Learning

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 incorporates causal knowledge into both the training and generation processes. CA-GAN first extracts a causal graph from real data to provide structural prior knowledge, then employs a graph-conditioned Conditional WGAN-GP whose sub-generators model variables according to their causal dependencies. More importantly, we introduce a reinforcement learning-based objective that treats causal graph discrepancy between real and synthetic data as a reward signal, enabling causal consistency to become an explicit optimization target during training rather than an implicit consequence of sampling order. Extensive experiments on 14 synthetic and real-world datasets demonstrate that CA-GAN consistently outperforms seven state-of-the-art baselines in causal preservation while achieving strong downstream utility, privacy preservation, and data quality. These results show that CA-GAN provides an effective and practical solution for generating high-quality synthetic tabular data that better respects underlying causal mechanisms.