首个融合大模型的因果公平数据生成方法，提升医疗数据公平性。
FairCauseSyn: Towards Causally Fair LLM-Augmented Synthetic Data Generation
- 用大模型增强生成，保留因果结构以提升公平性
- 生成数据在因果公平度上偏差小于10%
- 训练模型后敏感属性偏见降低70%，适合医疗研究
合成数据生成通过生成模型基于真实数据创建新数据。在医疗应用中，生成高质量且保持敏感属性公平性的数据对实现公平结果至关重要。现有基于GAN和大模型的方法主要关注反事实公平性，多用于金融与法律领域。因果公平性通过保留因果结构提供更全面的评估框架，但当前合成数据生成方法尚未在医疗场景中解决此问题。为此，我们提出首个结合大模型的合成数据生成方法，以增强因果公平性，使用真实世界表格型医疗数据。生成数据在因果公平性指标上偏离真实数据不足10%。使用因果公平预测器训练时，合成数据相较真实数据将敏感属性偏见降低70%。本工作提升了公平合成数据的可及性，支持更公平的医疗研究与医疗服务。
原文摘要 · Abstract (English)
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.
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