Replication data for: Causal Inference with Differential Measurement Error: Nonparametric Identification and Sensitivity Analysis

Political scientists have long been concerned about the validity of survey measurements. Although many have studied classical measurement error in linear regression models where the error is assumed to arise completely at random, in a number of situations the error may be correlated with the outcome. We analyze the impact of differential measurement error on causal estimation. The proposed nonparametric identification analysis avoids arbitrary modeling decisions and formally characterizes the roles of different assumptions. We show the serious consequences of differential misclassification and offer a new sensitivity analysis that allows researchers to evaluate the robustness of their conclusions. Our methods are motivated by a field experiment on democratic deliberations, in which one set of estimates potentially suffers from differential misclassification. We show that an analysis ignoring differential measurement error may considerably overestimate the causal effects. This finding contrasts with the case of classical measurement error which always yields attenuation bias.

政治学者长期以来一直关注调查测量的效度（validity）。尽管诸多研究者已在线性回归模型（linear regression models）中探讨了经典测量误差（classical measurement error）问题——此类模型假设误差完全随机产生，但在不少场景下，误差可能与结果变量相关。本文分析了差分测量误差（differential measurement error）对因果估计（causal estimation）的影响。本文提出的非参数识别分析（nonparametric identification analysis）规避了任意性建模决策，并对各类假设的作用进行了形式化刻画。本文揭示了差分错分（differential misclassification）的严重后果，并提出了全新的敏感性分析（sensitivity analysis）方法，可供研究者评估其结论的稳健性。本文的研究方法源于一项针对民主协商的现场实验（field experiment），该实验中的一组估计值可能存在差分错分问题。本文表明，忽略差分测量误差的分析可能会大幅高估因果效应。这一发现与经典测量误差的情形形成鲜明对比——后者总会导致衰减偏误（attenuation bias）。