arXiv Artificial Intelligence

Causal-Aware Tabular GANs with Reinforcement Learning

1 hour ago 02.10.2026 07:00 👁 1 views
Causal-Aware Tabular GANs with Reinforcement Learning

Quick summary

arXiv:2510.24046v2 Announce Type: replace-cross 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 incorporates causal knowledge into both the training and generation processes. CA-GAN first extracts a

Key takeaways

  • arXiv:2510.24046v2 Announce Type: replace-cross 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 incorporates causal knowledge into both the training and generation processes.

Why it matters

The importance of “Causal-Aware Tabular GANs with Reinforcement Learning” will be measured by what changes in practice. User behavior, access conditions, verifiable performance and responsible-use outcomes are the signals worth following.

Kaynak sitede devamını oku: arXiv Artificial Intelligence ↗