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Multivariate Continuous Blocking to Improve Political Science Experiments

Published online by Cambridge University Press:  04 January 2017

Ryan T. Moore
Ryan T. Moore*
Affiliation:
University of California—Berkeley and University of California—San Francisco, 50 University Hall, MC7360, Berkeley CA 94720-7360; Department of Political Science, Washington University in St. Louis, 241 Seigle Hall, Campus Box 1063, One Brookings Drive, St. Louis MO 63130. e-mail: rtm@wustl.edu
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Abstract

Political scientists use randomized treatment assignments to aid causal inference in field experiments, psychological laboratories, and survey research. Political research can do considerably better than completely randomized designs, but few political science experiments combine random treatment assignment with blocking on a rich set of background covariates. We describe high-dimensional multivariate blocking, including on continuous covariates, detail its statistical and political advantages over complete randomization, introduce a particular algorithm, and propose a procedure to mitigate unit interference in experiments. We demonstrate the performance of our algorithm in simulations and three field experiments from campaign politics and education.

Type
Research Article
Information
Political Analysis , Volume 20 , Issue 4 , Autumn 2012 , pp. 460 - 479
Copyright
Copyright © The Author 2012. Published by Oxford University Press on behalf of the Society for Political Methodology 

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