Generative Synthetic Data for Causal Inference: Pitfalls, Remedies, and Opportunities
用于因果推断的生成式合成数据：陷阱、补救与机会AI 解读 ↓
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用于因果推断的生成式合成数据：陷阱、补救与机会
- 研究问题
- 预测表现或分布相似度良好的合成数据，是否仍会扭曲平均处理效应？处理不平衡与重叠不足如何加剧这种偏差？
- 方法与思路
- 作者通过敏感性分析与损失分解解释预测目标和因果目标的差异，并提出生成协变量、分别建模处理与结果机制的混合框架。
- 材料陈述的贡献
- 摘要称，混合合成在受控模拟中改善因果保真度，并展示其在正值性不足下的数据增强及真实分析前估计量比较中的用途。
- 阅读关注点
- 应核对效应保真的条件、重叠加权损失的推导，以及混合机制设定、模型误设检查和真实应用中因果保真度的评价依据。
- 阅读边界
- 这是 arXiv 预印本，同行评审与期刊接收未核实；摘要未给出具体识别条件与数值结果，应用中的预测改善不能直接证明因果有效性。
原文摘要
Synthetic tabular data are often evaluated by distributional similarity, privacy distance, or train-on-synthetic-test-on-real predictive performance, but these criteria do not ensure validity for causal inference. We show that fully generative tabular synthesizers, including GAN- and LLM-based models, can preserve predictive utility while distorting average treatment effect (ATE) estimates. The failure is structural: ATE preservation requires both a realistic covariate law and an accurate treatment-effect contrast, whereas prediction loss penalizes treatment-effect error only through an overlap-weighted term. Thus, under imbalance or limited overlap, a generator may reproduce dominant observed outcomes while underlearning intervention-relevant contrasts. We formalize this mismatch through sensitivity and loss-decomposition results. Motivated by this causal analysis and intuition, we propose a hybrid synthetic-data framework for causal inference that generates covariates while modeling treatment and outcome mechanisms separately. We evaluate the framework in three settings: ATE preservation under fully generative versus hybrid synthesis, augmentation for practical positivity problems, and diagnostic simulation engines for comparing OR, IPW, AIPW, and TMLE before real-data analysis. We also stress-test the hybrid construction across settings that vary overlap, covariate dimension, seed sample size, and treatment-effect complexity, including a logistic outcome-model misspecification check. Across controlled simulation experiments, hybrid synthesis improves causal fidelity relative to fully generative baselines; the ACTG application shows improved predictive fidelity and potential for finite-sample estimator benchmarking. LLM-based hybrid synthesis is often more faithful than CTGAN in settings where causal fidelity can be assessed.