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Research Note: A More Powerful Test Statistic for Reasoning about Interference between Units

Published online by Cambridge University Press:  04 January 2017

Jake Bowers ,
Mark M. Fredrickson  and
Peter M. Aronow
Jake Bowers*
Affiliation:
Department of Political Science and Statistics, University of Illinois at Urbana-Champaign, 420 David Kinley Hall (DKH) MC-713, 1407 W Gregory Dr, Urbana, IL 61801, USA, [email protected]
Mark M. Fredrickson
Affiliation:
Department of Political Science and Statistics, University of Illinois at Urbana-Champaign, 420 David Kinley Hall (DKH) MC-713, 1407 W Gregory Dr, Urbana, IL 61801, USA, [email protected]
Peter M. Aronow
Affiliation:
Department of Political Science and Biostatistics, Yale University, 77 Prospect Street, New Haven, CT 06520, USA, e-mail: [email protected]
*
e-mail: [email protected] (corresponding author);
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Abstract

Bowers, Fredrickson, and Panagopoulos (2013, Reasoning about interference between units: A general framework, Political Analysis 21(1):97–124; henceforth BFP) showed that one could use Fisher's randomization-based hypothesis testing framework to assess counterfactual causal models of treatment propagation and spillover across social networks. This research note improves the statistical inference presented in BFP (2013) by substituting a test statistic based on a sum of squared residuals and incorporating information about the fixed network for the simple Kolmogorov–Smirnov test statistic (Hollander 1999, section 5.4) they used. This note incrementally improves the application of BFP's “reasoning about interference” approach. We do not offer general results about test statistics for multi-parameter causal models on social networks here, but instead hope to stimulate further, and deeper, work on test statistics and sharp hypothesis testing.

Type
Letters
Information
Political Analysis , Volume 24 , Issue 3 , Summer 2016 , pp. 395 - 403
Copyright
Copyright © The Author 2016. Published by Oxford University Press on behalf of the Society for Political Methodology 

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Footnotes

Authors’ note: Data and code to reproduce this document can be found at Bowers, Fredrickson and Aronow (2016).

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