# Causal-Aware Tabular GANs with Reinforcement Learning

arXiv: https://arxiv.org/abs/2510.24046 · HTML: https://mlsanity.com/paper/2510.24046/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Placed in RL with diffusion and flow models, one of 15 directions in Deep reinforcement learning methods.
- Builds on 17 of its 28 references that are in the map.
- First posted on arXiv 28 Oct 2025.

## Placed in

- Deep reinforcement learning methods / [RL with diffusion and flow models](https://mlsanity.com/deeprl/rl-with-diffusion-and-flow-models/)

## Builds on (most cited first, 17 of 17)

- [XGBoost: A Scalable Tree Boosting System](https://mlsanity.com/paper/1603.02754/) (2016)
- [Improved Training of Wasserstein GANs](https://mlsanity.com/paper/1704.00028/) (2017)
- [Modeling Tabular data using Conditional GAN](https://mlsanity.com/paper/1907.00503/) (2019)
- [Tabular Data: Deep Learning is Not All You Need](https://mlsanity.com/paper/2106.03253/) (2021)
- [Language Models are Realistic Tabular Data Generators](https://mlsanity.com/paper/2210.06280/) (2022)
- [Causal Inference in the Presence of Latent Variables and Selection Bias](https://mlsanity.com/paper/1302.4983/) (2013)
- [CausalGAN: Learning Causal Implicit Generative Models with Adversarial Training](https://mlsanity.com/paper/1709.02023/) (2017)
- [DECAF: Generating Fair Synthetic Data Using Causally-Aware Generative Networks](https://mlsanity.com/paper/2110.12884/) (2021)
- [TabDiff: a Mixed-type Diffusion Model for Tabular Data Generation](https://mlsanity.com/paper/2410.20626/) (2024)
- [Generative Table Pre-training Empowers Models for Tabular Prediction](https://mlsanity.com/paper/2305.09696/) (2023)
- [Fast Scalable and Accurate Discovery of DAGs Using the Best Order Score Search and Grow-Shrink Trees](https://mlsanity.com/paper/2310.17679/) (2023)
- [The Inadequacy of Similarity-based Privacy Metrics: Privacy Attacks against "Truly Anonymous" Synthetic Datasets](https://mlsanity.com/paper/2312.05114/) (2023)
- [Generating Realistic Tabular Data with Large Language Models](https://mlsanity.com/paper/2410.21717/) (2024)
- [Doubling Your Data in Minutes: Ultra-fast Tabular Data Generation via LLM-Induced Dependency Graphs](https://mlsanity.com/paper/2507.19334/) (2025)
- [TabStruct: Measuring Structural Fidelity of Tabular Data](https://mlsanity.com/paper/2509.11950/) (2025)
- [Synthetic Tabular Data Generation: A Comparative Survey for Modern Techniques](https://mlsanity.com/paper/2507.11590/) (2025)
- [On the causality-preservation capabilities of generative modelling](https://mlsanity.com/paper/2301.01109/) (2023)

## Built on by (most cited first, 1 of 1)

- [Improving TabPFN's Synthetic Data Generation by Integrating Causal Structure](https://mlsanity.com/paper/2603.10254/) (2026)

## Abstract (arXiv)

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.