Skip to main content Accessibility help
×
Hostname: page-component-7c8c6479df-24hb2 Total loading time: 0 Render date: 2024-03-17T08:12:46.632Z Has data issue: false hasContentIssue false

10 - Pairwise Randomized Experiments

from PART II - CLASSICAL RANDOMIZED EXPERIMENTS

Published online by Cambridge University Press:  05 May 2015

Guido W. Imbens
Affiliation:
Stanford University, California
Donald B. Rubin
Affiliation:
Harvard University, Massachusetts
Get access

Summary

INTRODUCTION

In the previous chapter we analyzed stratified randomized experiments, where a sample of size N was partitioned into J strata, and within each stratum a completely randomized experiment was conducted. In this chapter we consider a special case of the stratified randomized experiment. Each stratum contains exactly two units, with one randomly selected to be assigned to the treatment group, and the other one assigned to the control group. Such a design is known as a pairwise randomized experiment or paired comparison. Although this can be viewed simply as a special case of a stratified randomized experiment, there are two features of this design that warrant special attention. First, the fact that there is only a single unit in each treatment group in each stratum (or pair in this case) implies that the Neyman sampling variance estimator that we discussed in the chapters on completely randomized experiments (Chapter 6) and stratified randomized experiments (Chapter 9) cannot be used; that estimator requires the presence of at least two units assigned to each treatment in each stratum. Second, each stratum has the same proportion of treated units, which allows us to analyze the within-stratum estimates symmetrically; the natural estimator for the average treatment effect weights each stratum equally.

As in the case of stratified randomized experiments, the motivation for eliminating some of the possible assignments in pairwise randomized experiments is that a priori those values of the assignment vectors that are eliminated are expected to lead to less informative inferences. This argument relies on the within-pair variation in potential outcomes being small relative to the between-pair variation. Often the assignment to pairs is based on covariates. Units are matched to other units based on their similarity in these covariates, with the expectation that this similarity corresponds to similarity in the potential outcomes under each treatment. Suppose, for example, that the treatment is an expensive surgical procedure for a relatively common medical condition. It may not be financially feasible to apply the treatment to many individuals.

Type
Chapter
Information
Publisher: Cambridge University Press
Print publication year: 2015

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

Save book to Kindle

To save this book to your Kindle, first ensure coreplatform@cambridge.org is added to your Approved Personal Document E-mail List under your Personal Document Settings on the Manage Your Content and Devices page of your Amazon account. Then enter the ‘name’ part of your Kindle email address below. Find out more about saving to your Kindle.

Note you can select to save to either the @free.kindle.com or @kindle.com variations. ‘@free.kindle.com’ emails are free but can only be saved to your device when it is connected to wi-fi. ‘@kindle.com’ emails can be delivered even when you are not connected to wi-fi, but note that service fees apply.

Find out more about the Kindle Personal Document Service.

Available formats
×

Save book to Dropbox

To save content items to your account, please confirm that you agree to abide by our usage policies. If this is the first time you use this feature, you will be asked to authorise Cambridge Core to connect with your account. Find out more about saving content to Dropbox.

Available formats
×

Save book to Google Drive

To save content items to your account, please confirm that you agree to abide by our usage policies. If this is the first time you use this feature, you will be asked to authorise Cambridge Core to connect with your account. Find out more about saving content to Google Drive.

Available formats
×