Unbiased Estimation of the Average Treatment Effect in Cluster-Randomized Experiments

54 Pages Posted: 8 Apr 2011  

Joel A. Middleton

New York University (NYU) - The Steinhardt School

Peter M. Aronow

Yale University - Department of Political Science

Date Written: April 5, 2011

Abstract

Many estimators of the average treatment effect, including difference-in-means, may be biased when clusters of units are allocated to treatment. This bias may remain even when the number of units grows asymptotically large. In this paper, we propose simple, unbiased and scale-invariant design-based estimators of the average treatment effect, along with associated variance estimators. We then analyze a cluster-randomized field experiment on voter mobilization in the United States, demonstrating that the proposed estimators have precision that is comparable (if not superior) to that of existing biased estimators of the average treatment effect. Our results have methodological implications for both experimental and observational research reliant on the Neyman-Rubin Causal Model of potential outcomes.

Keywords: causal inference, cluster-randomized experiments, experimental methodology, field experiments, group-randomized trials, potential outcomes, Neyman-Rubin Causal Model

JEL Classification: C9, C90, C93, C00

Suggested Citation

Middleton, Joel A. and Aronow, Peter M., Unbiased Estimation of the Average Treatment Effect in Cluster-Randomized Experiments (April 5, 2011). Available at SSRN: https://ssrn.com/abstract=1803849 or http://dx.doi.org/10.2139/ssrn.1803849

Joel A. Middleton

New York University (NYU) - The Steinhardt School ( email )

New York, NY
United States

Peter Michael Aronow (Contact Author)

Yale University - Department of Political Science ( email )

P.O. Box 208301
New Haven, CT 06520-8269
United States

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