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Tree‐Based Models for Political Science Data

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  • Jacob M. Montgomery
  • Santiago Olivella

Abstract

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.

Suggested Citation

  • Jacob M. Montgomery & Santiago Olivella, 2018. "Tree‐Based Models for Political Science Data," American Journal of Political Science, John Wiley & Sons, vol. 62(3), pages 729-744, July.
  • Handle: RePEc:wly:amposc:v:62:y:2018:i:3:p:729-744
    DOI: 10.1111/ajps.12361
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    Cited by:

    1. Nicolaj N. Mühlbach, 2020. "Tree-based Synthetic Control Methods: Consequences of moving the US Embassy," CREATES Research Papers 2020-04, Department of Economics and Business Economics, Aarhus University.
    2. Waggoner Philip D. & Kennedy Ryan & Le Hayden & Shiran Myriam, 2019. "Big Data and Trust in Public Policy Automation," Statistics, Politics and Policy, De Gruyter, vol. 10(2), pages 115-136, December.
    3. Zhaochen He & John Camobreco & Keith Perkins, 2022. "How he won: Using machine learning to understand Trump’s 2016 victory," Journal of Computational Social Science, Springer, vol. 5(1), pages 905-947, May.
    4. Andrew B Whetten & John R Stevens & Damon Cann, 2021. "The implementation of random survival forests in conflict management data: An examination of power sharing and third party mediation in post-conflict countries," PLOS ONE, Public Library of Science, vol. 16(5), pages 1-16, May.

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