What can we learn from predictive modeling?

Skyler J. Cranmer, Bruce A. Desmarais

Research output: Contribution to journalArticlepeer-review

26 Scopus citations


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 reviewthe central benefits of this under-utilized approach from a perspective uncommon in the existing literature:we focus on howpredictive 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.

Original languageEnglish (US)
Pages (from-to)145-166
Number of pages22
JournalPolitical Analysis
Issue number2
StatePublished - Apr 1 2017

All Science Journal Classification (ASJC) codes

  • Sociology and Political Science
  • Political Science and International Relations


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