FairCauseSyn: Towards Causally Fair LLM-Augmented Synthetic Data Generation
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arXiv
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| Autori principali: | , , |
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| Natura: | Preprint |
| Pubblicazione: |
2025
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| _version_ | 1866908418761031680 |
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| author | Nagesh, Nitish Wang, Ziyu Rahmani, Amir M. |
| author_facet | Nagesh, Nitish Wang, Ziyu Rahmani, Amir M. |
| contents | Synthetic data generation creates data based on real-world data using generative models. In health applications, generating high-quality data while maintaining fairness for sensitive attributes is essential for equitable outcomes. Existing GAN-based and LLM-based methods focus on counterfactual fairness and are primarily applied in finance and legal domains. Causal fairness provides a more comprehensive evaluation framework by preserving causal structure, but current synthetic data generation methods do not address it in health settings. To fill this gap, we develop the first LLM-augmented synthetic data generation method to enhance causal fairness using real-world tabular health data. Our generated data deviates by less than 10% from real data on causal fairness metrics. When trained on causally fair predictors, synthetic data reduces bias on the sensitive attribute by 70% compared to real data. This work improves access to fair synthetic data, supporting equitable health research and healthcare delivery. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2506_19082 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | FairCauseSyn: Towards Causally Fair LLM-Augmented Synthetic Data Generation Nagesh, Nitish Wang, Ziyu Rahmani, Amir M. Machine Learning Artificial Intelligence Synthetic data generation creates data based on real-world data using generative models. In health applications, generating high-quality data while maintaining fairness for sensitive attributes is essential for equitable outcomes. Existing GAN-based and LLM-based methods focus on counterfactual fairness and are primarily applied in finance and legal domains. Causal fairness provides a more comprehensive evaluation framework by preserving causal structure, but current synthetic data generation methods do not address it in health settings. To fill this gap, we develop the first LLM-augmented synthetic data generation method to enhance causal fairness using real-world tabular health data. Our generated data deviates by less than 10% from real data on causal fairness metrics. When trained on causally fair predictors, synthetic data reduces bias on the sensitive attribute by 70% compared to real data. This work improves access to fair synthetic data, supporting equitable health research and healthcare delivery. |
| title | FairCauseSyn: Towards Causally Fair LLM-Augmented Synthetic Data Generation |
| topic | Machine Learning Artificial Intelligence |
| url | https://arxiv.org/abs/2506.19082 |