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What Can We Learn from Predictive Modeling?

  • Skyler J. Cranmer (a1) and Bruce A. Desmarais (a2)

The large majority of inferences drawn in empirical political research follow from model-based associations (e.g., regression). Here, we articulate the benefits of predictive modeling as a complement to this approach. Predictive models aim to specify a probabilistic model that provides a good fit to testing data that were not used to estimate the model’s parameters. Our goals are threefold. First, we review the central benefits of this under-utilized approach from a perspective uncommon in the existing literature: we focus on how predictive modeling can be used to complement and augment standard associational analyses. Second, we advance the state of the literature by laying out a simple set of benchmark predictive criteria. Third, we illustrate our approach through a detailed application to the prediction of interstate conflict.

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Authors’ note: Many thanks to Alison Craig for research assistance. Sincere thanks also to Matt Blackwell and Michael Neblo for helpful comments on an earlier draft. The authors are grateful for the support of the National Science Foundation (SES-1558661, SES-1619644, SES-1637089, CISE-1320219, SES-1357622, SES-1514750, and SES-1461493) and the Alexander von Humboldt Foundation. Replication data are posted to the Political Analysis Dataverse (Cranmer and Desmarais 2016a).

Contributing Editor: Jonathan Katz

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