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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.
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