Tree-Based Models for Political Science Data

Jacob M. Montgomery, Santiago Olivella

Research output: Contribution to journalArticlepeer-review

21 Scopus citations


Political scientists often find themselves analyzing data sets with a large number of observations, a large number of variables, or both. Yet, traditional statistical techniques fail to take full advantage of the opportunities inherent in “big data,” as they are too rigid to recover nonlinearities and do not facilitate the easy exploration of interactions in high-dimensional data sets. In this article, we introduce a family of tree-based nonparametric techniques that may, in some circumstances, be more appropriate than traditional methods for confronting these data challenges. In particular, tree models are very effective for detecting nonlinearities and interactions, even in data sets with many (potentially irrelevant) covariates. We introduce the basic logic of tree-based models, provide an overview of the most prominent methods in the literature, and conduct three analyses that illustrate how the methods can be implemented while highlighting both their advantages and limitations.

Original languageEnglish (US)
Pages (from-to)729-744
Number of pages16
JournalAmerican Journal of Political Science
Issue number3
StatePublished - Jul 2018

ASJC Scopus subject areas

  • Sociology and Political Science
  • Political Science and International Relations


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