论文标题
推断估算数据：造型的诱惑
Inference with Imputed Data: The Allure of Making Stuff Up
论文作者
论文摘要
数据的不完整可观察性会产生识别问题。没有灵丹妙药丢失数据。关于人口参数的人可以学到什么取决于人们认为可靠的假设。假设的可信度随经验环境而异。没有具体的假设可以为缺少数据的推理提供一个现实的通用解决方案。然而，鲁宾已将随机多重插补（RMI）作为处理公共用途数据中缺失价值的一般方法。该建议对经验研究人员的影响很大，他们寻求简单的解决方案，以解决丢失数据的滋扰。本文增加了我早期的归纳批评。它对鲁宾为RMI争辩的贝叶斯和频繁思维的混合提供了透明的评估。当目标是学习有条件的期望时，它评估随机插补以替换缺失的结果或协变量数据。它考虑了可能有助于打击造型的诱惑的步骤。
Incomplete observability of data generates an identification problem. There is no panacea for missing data. What one can learn about a population parameter depends on the assumptions one finds credible to maintain. The credibility of assumptions varies with the empirical setting. No specific assumptions can provide a realistic general solution to the problem of inference with missing data. Yet Rubin has promoted random multiple imputation (RMI) as a general way to deal with missing values in public-use data. This recommendation has been influential to empirical researchers who seek a simple fix to the nuisance of missing data. This paper adds to my earlier critiques of imputation. It provides a transparent assessment of the mix of Bayesian and frequentist thinking used by Rubin to argue for RMI. It evaluates random imputation to replace missing outcome or covariate data when the objective is to learn a conditional expectation. It considers steps that might help combat the allure of making stuff up.
