FairTabGen: Unifying Counterfactual and Causal Fairness in Synthetic Tabular Data Generation

• URL: http://arxiv.org/abs/2508.11810v1
• Date: Fri, 15 Aug 2025 21:36:07 GMT
• Title: FairTabGen: Unifying Counterfactual and Causal Fairness in Synthetic Tabular Data Generation
• Authors: Nitish Nagesh, Salar Shakibhamedan, Mahdi Bagheri, Ziyu Wang, Nima TaheriNejad, Axel Jantsch, Amir M. Rahmani,
• Abstract summary: We present FairTabGen, a fairness-aware large language model-based framework for synthetic data generation.<n>We use in-context learning, prompt refinement, and fairness-aware data curation to balance fairness and utility.
• Score: 4.044506553590468
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Generating synthetic data is crucial in privacy-sensitive, data-scarce settings, especially for tabular datasets widely used in real-world applications. A key challenge is improving counterfactual and causal fairness, while preserving high utility. We present FairTabGen, a fairness-aware large language model-based framework for tabular synthetic data generation. We integrate multiple fairness definitions including counterfactual and causal fairness into both its generation and evaluation pipelines. We use in-context learning, prompt refinement, and fairness-aware data curation to balance fairness and utility. Across diverse datasets, our method outperforms state-of-the-art GAN-based and LLM-based methods, achieving up to 10% improvements on fairness metrics such as demographic parity and path-specific causal effects while retaining statistical utility. Remarkably, it achieves these gains using less than 20% of the original data, highlighting its efficiency in low-data regimes. These results demonstrate a principled and practical approach for generating fair and useful synthetic tabular data.

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