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

  • Jake Bowers (a1), Mark M. Fredrickson (a1) and Peter M. Aronow (a2)

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.

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

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Aronow, Peter M., and Samii, Cyrus. 2012. Estimating average causal effects under general interference. Unpublished manuscript.
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.
Bowers, Jake, Fredrickson, Mark M., and Panagopoulos, Costas. 2013. Reasoning about interference between units: a general framework. Political Analysis 21(1):97124.
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Political Analysis
  • ISSN: 1047-1987
  • EISSN: 1476-4989
  • URL: /core/journals/political-analysis
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