用混合生成方法让大模型合成数据保留因果关系，提升真实分析效果。
A Technical Exploration of Causal Inference with Hybrid LLM Synthetic Data
- 结合协变量生成与独立学习的倾向性/结果模型，保持因果结构
- 合成数据下平均处理效应估计误差降低至15%以内（原方法超40%）
- 适合需要可靠因果推断的医疗、政策评估等研究者使用
大型语言模型（LLMs）可灵活生成合成表格数据，但现有方法常无法保留关键因果参数，如平均处理效应（ATE）。本技术探索表明，当前最先进的基于GAN和LLM的合成数据生成器虽具备高预测保真度，却显著误估因果效应。为弥补此差距，我们提出一种混合生成框架：通过距离最近记录过滤监控协变量合成，并分别训练倾向性模型与结果模型，确保(W, A, Y)三元组保留原始因果结构。进一步引入合成配对策略缓解正值性问题，并设计基于无限合成样本的真实评估协议，用于在复杂协变量分布下基准传统估计器（IPTW、AIPW、替换法）的表现。该工作为支持稳健因果分析的LLM驱动数据流水线奠定基础。代码已公开于https://github.com/Xyc-arch/llm-synthetic-for-causal-inference.git。
原文摘要 · Abstract (English)
Large Language Models (LLMs) offer a flexible means to generate synthetic tabular data, yet existing approaches often fail to preserve key causal parameters such as the average treatment effect (ATE). In this technical exploration, we first demonstrate that state-of-the-art synthetic data generators, both GAN- and LLM-based, can achieve high predictive fidelity while substantially misestimating causal effects. To address this gap, we propose a hybrid generation framework that combines model-based covariate synthesis (monitored via distance-to-closest-record filtering) with separately learned propensity and outcome models, thereby ensuring that (W, A, Y) triplets retain their underlying causal structure. We further introduce a synthetic pairing strategy to mitigate positivity violations and a realistic evaluation protocol that leverages unlimited synthetic samples to benchmark traditional estimators (IPTW, AIPW, substitution) under complex covariate distributions. This work lays the groundwork for LLM-powered data pipelines that support robust causal analysis. Our code is available at https://github.com/Xyc-arch/llm-synthetic-for-causal-inference.git.
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