Title:      Causal Inference with Measurement Error:
Nonparametric Identification and Sensitivity Analyses of a Field
Experiment on Democratic Deliberations

Authors:    Kosuke Imai, Teppei Yamamoto

Entrydate:  2008-06-30 16:58:00

Keywords:   differential misclassification, nonparametric
bounds, retrospective studies, sensitivity analysis, survey
measurements

Abstract:   Political scientists have long been concerned about
the validity of survey measurements.  Indeed, given the
increasing use of survey experiments, measurement error
represents a threat to causal inference.  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.  Such differential measurement error often arises in
retrospective studies where the treatment is measured after the
outcome is realized.  We analyze the impact of differential
measurement error on causal estimation by deriving the sharp
bounds of the average treatment effect.  The proposed
nonparametric identification analysis avoids arbitrary modeling
decisions and formally characterizes the roles of additional
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.  The finding
contrasts with the case of classical measurement error which
always yields attenuation bias.

http://polmeth.wustl.edu/retrieve.php?id=763

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