AI-Generated Measurements for Identification and Inference with Missing Data: A Weak Shadow Variable Approach
利用 AI 生成测量识别缺失数据：弱影子变量方法AI 解读 ↓
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利用 AI 生成测量识别缺失数据：弱影子变量方法
- 研究问题
- 当结局缺失依赖未观测结局本身时，能否利用非结构化数据生成的辅助测量，在较少限制下界定总体量并开展推断？
- 方法与思路
- 作者将辅助测量定义为给定真实结局和协变量后独立于缺失的弱影子变量，以线性规划求锐界，并用惩罚估计和子抽样推断。
- 材料陈述的贡献
- 摘要称无需准确预测缺失结局或满足完备性即可构造锐界；客服对话半合成实验中，区间宽度与中点误差均有改善。
- 阅读关注点
- 应核对弱影子变量条件能否由应用机制支持、线性规划的锐性证明，以及测量构造误差和抽样误差如何进入区间。
- 阅读边界
- 这是 arXiv 预印本，同行评审或期刊接收未获核实；半合成结果不保证真实缺失机制下有效，条件独立性仍需论证。
原文摘要
Across business and social science applications, outcomes are often missing in ways that depend on the unobserved outcomes themselves. In service systems, for example, whether a customer submits a rating depends on the rating they would have provided. Such missing-not-at-random (MNAR) mechanisms make population quantities difficult to identify without strong assumptions on the observation process. Meanwhile, rich unstructured data, such as customer interaction histories, are increasingly available and can be used to construct structured measurements using tools such as large language models (LLMs). In this work, we develop an assumption-lean partial identification framework that uses such measurements as weak shadow variables, defined as outcome-informative proxies that are conditionally independent of missingness given the true outcome and observed covariates. Importantly, they need not accurately predict missing outcomes or satisfy the completeness requirement in the classical shadow variable literature. For identification, we characterize sharp bounds on population quantities through a pair of linear programs. For estimation and inference, we propose a localized penalized estimator that remains feasible under sampling error, and a subsampling algorithm for constructing confidence intervals. In semi-synthetic experiments using real customer-service dialogues, weak-shadow-variable intervals are about 89\% narrower than those without auxiliary information, while their midpoints have around 41\% lower estimation error than classical MNAR methods.