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

Political scientists have long been concerned about the validity of survey measurements Although ninny 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 Thus finding contrasts with the case of classical measurement error, which always yields attenuation bias.