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Estimating Treatment Effects in the Presence of Noncompliance and Nonresponse: The Generalized Endogenous Treatment Model

Published online by Cambridge University Press:  04 January 2017

Kevin M. Esterling*
Affiliation:
Department of Political Science, UC—Riverside, 900 University Ave., Riverside, CA 92506
Michael A. Neblo
Affiliation:
Department of Political Science, Ohio State University, 2114 Derby Hall, 154 N Oval Mall, Columbus, OH 43210 e-mail: neblo.1@osu.edu
David M. J. Lazer
Affiliation:
Departments of Political Science and Computer Science, Northeastern University, 301 Meserve Hall, Boston, MA 02115 e-mail: d.lazer@neu.edu
*
e-mail: kevin.esterling@ucr.edu (corresponding author)

Abstract

If ignored, noncompliance with a treatment or nonresponse on outcome measures can bias estimates of treatment effects in a randomized experiment. To identify and estimate causal treatment effects in the case where compliance and response depend on unobservables, we propose the parametric generalized endogenous treatment (GET) model. GET incorporates behavioral responses within an experiment to measure each subject's latent compliance type and identifies causal effects via principal stratification. Using simulation methods and an application to field experimental data, we show GET has a dramatically lower mean squared error for treatment effect estimates than existing approaches to principal stratification that impute, rather than measure, compliance type. In addition, we show that GET allows one to relax and test the instrumental variable exclusion restriction assumption, to test for the presence of treatment effect heterogeneity across a range of compliance types, and to test for treatment ignorability when treatment and control samples are balanced on observable covariates.

Type
Regular Articles
Copyright
Copyright © The Author 2011. Published by Oxford University Press on behalf of the Society for Political Methodology 

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