Article contents
Blocking for Sequential Political Experiments
Published online by Cambridge University Press: 04 January 2017
Abstract
In typical political experiments, researchers randomize a set of households, precincts, or individuals to treatments all at once, and characteristics of all units are known at the time of randomization. However, in many other experiments, subjects “trickle in” to be randomized to treatment conditions, usually via complete randomization. To take advantage of the rich background data that researchers often have (but underutilize) in these experiments, we develop methods that use continuous covariates to assign treatments sequentially. We build on biased coin and minimization procedures for discrete covariates and demonstrate that our methods outperform complete randomization, producing better covariate balance in simulated data. We then describe how we selected and deployed a sequential blocking method in a clinical trial and demonstrate the advantages of our having done so. Further, we show how that method would have performed in two larger sequential political trials. Finally, we compare causal effect estimates from differences in means, augmented inverse propensity weighted estimators, and randomization test inversion.
- Type
- Research Article
- Information
- Copyright
- Copyright © The Author 2013. Published by Oxford University Press on behalf of the Society for Political Methodology
Footnotes
Authors' note: We thank Nicholas Beauchamp, Jens Hainmueller, Kosuke Imai, Rebecca Morton, Kevin Quinn, and the participants in EGAP 6 and ICHPS 9 for helpful suggestions. The replication archive is available at Moore and Moore (2013). Supplementary materials for the article are available on the Political Analysis Web site.
References
- 6
- Cited by