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Combining Double Sampling and Bounds to Address Nonignorable Missing Outcomes in Randomized Experiments

Author(s): 

Alexander Coppock, Alan S. Gerber, Donald P. Green, Holger L. Kern

ISPS ID: 
ISPS17-07
Full citation: 
Coppock, Alexander, Alan S. Gerber, Donald P. Green, Holger L. Kern (2017). Combining Double Sampling and Bounds to Address Nonignorable Missing Outcomes in Randomized Experiments. Political Analysis. Published online: 23 February 2017. DOI: 10.1017/pan.2016.6.
Abstract: 
Missing outcome data plague many randomized experiments. Common solutions rely on ignorability assumptions that may not be credible in all applications. We propose a method for confronting missing outcome data that makes fairly weak assumptions but can still yield informative bounds on the average treatment effect. Our approach is based on a combination of the double sampling design and nonparametric worst-case bounds. We derive a worst-case bounds estimator under double sampling and provide analytic expressions for variance estimators and confidence intervals. We also propose a method for covariate adjustment using poststratification and a sensitivity analysis for nonignorable missingness. Finally, we illustrate the utility of our approach using Monte Carlo simulations and a placebo-controlled randomized field experiment on the effects of persuasion on social attitudes with survey-based outcome measures.
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Publication date: 
2017
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