Hostname: page-component-848d4c4894-m9kch Total loading time: 0 Render date: 2024-06-02T23:49:23.256Z Has data issue: false hasContentIssue false
  • English
  • Français

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, fredric3@illinois.edu
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, fredric3@illinois.edu
Peter M. Aronow
Affiliation:
Department of Political Science and Biostatistics, Yale University, 77 Prospect Street, New Haven, CT 06520, USA, e-mail: peter.aronow@yale.edu
*
e-mail: jwbowers@illinois.edu (corresponding author);
Get access Rights & Permissions [Opens in a new window]

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 

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.)

Footnotes

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

References

Aronow, Peter M., and Samii, Cyrus. 2012. Estimating average causal effects under general interference. Unpublished manuscript.Google Scholar
Bowers, Jake, Fredrickson, Mark and Aronow, Peter M. 2016. Replication data for: Research Note: A more powerful test statistic for reasoning about interference between units. Harvard Dataverse. http://dx.doi.org/10.7910/DVN/V6ECYU.CrossRefGoogle Scholar
Bowers, Jake, Fredrickson, Mark M., and Panagopoulos, Costas. 2013. Reasoning about interference between units: a general framework. Political Analysis 21(1):97–124.CrossRefGoogle Scholar
Brady, Henry E. 2008. Causation and explanation in social science. In: Oxford handbook of political methodology, eds. Box-Steffensmeier, Janet M., Brady, Henry E. and David, Collier, 217–70. Oxford, UK: Oxford University Press, http://www.oxfordhandbooks.com/view/10.1093/oxfordhb/9780199286546.001.0001/oxfordhb-9780199286546.Google Scholar
Butler, Daniel M., and Nickerson, David W. 2011. Can learning constituency opinion affect how legislators vote? Results from a field experiment. Quarterly Journal of Political Science 6(1):55–83.CrossRefGoogle Scholar
Coppock, Alexander. 2014. Information spillovers: another look at experimental estimates of legislator responsiveness. Journal of Experimental Political Science 1(2):159–69.CrossRefGoogle Scholar
Fisher, Ronald A. 1935. The design of experiments. Edinburgh: Oliver and Boyd.Google Scholar
Holland, Paul W. 1986. Statistics and causal inference (with discussion). Journal of the American Statistical Association 81:945–70.Google Scholar
Hollander, Myles. 1999. Nonparametric statistical methods, 2nd ed. Hoboken, NJ: Wiley-Interscience.Google Scholar
Kish, Leslie 1965. Survey sampling. New York: John Wiley and Sons.Google Scholar
Rosenbaum, Paul R. 2002. Observational studies, 2nd ed. New York, NY: Springer.CrossRefGoogle Scholar
Rosenbaum, Paul R. 2010. Design of observational studies. New York, NY: Springer, http://www.springer.com/statistics/statistical+theory+and+methods/book/978-1-4419-1212-1.CrossRefGoogle ScholarPubMed
Szekely, Gábor J., and Rizzo, Maria L. 2009. Brownian distance covariance. Annals of Applied Statistics 3(4):1236–65.Google Scholar
Winters, Matthew S., Testa, Paul, and Fredrickson, Mark. 2012. Using field experiments to understand information as an antidote to corruption. Research in Experimental Economics 15:213–246, http://www.emeraldinsight.com/doi/abs/10.1108/S0193-2306%282012%290000015010.CrossRefGoogle Scholar