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What Can We Learn from Predictive Modeling?

Author

Listed:
  • Cranmer, Skyler J.
  • Desmarais, Bruce A.
Abstract
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.

Suggested Citation

  • Cranmer, Skyler J. & Desmarais, Bruce A., 2017. "What Can We Learn from Predictive Modeling?," Political Analysis, Cambridge University Press, vol. 25(2), pages 145-166, April.
    • Handle: RePEc:cup:polals:v:25:y:2017:i:02:p:145-166_00
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    Cited by:

    1. Tobias Heinrich & Yoshiharu Kobayashi, 2022. "Evaluating explanations for poverty selectivity in foreign aid," Kyklos, Wiley Blackwell, vol. 75(1), pages 30-47, February.
    2. Robert A. Blair & Nicholas Sambanis, 2020. "Forecasting Civil Wars: Theory and Structure in an Age of “Big Data†and Machine Learning," Journal of Conflict Resolution, Peace Science Society (International), vol. 64(10), pages 1885-1915, November.
    3. Dyevre, Arthur & Lampach, Nicolas, 2018. "The origins of regional integration: Untangling the effect of trade on judicial cooperation," International Review of Law and Economics, Elsevier, vol. 56(C), pages 122-133.
    4. Gallego, Jorge & Rivero, Gonzalo & Martínez, Juan, 2021. "Preventing rather than punishing: An early warning model of malfeasance in public procurement," International Journal of Forecasting, Elsevier, vol. 37(1), pages 360-377.
    5. Pamp, Oliver & Lebacher, Michael & Thurner, Paul W. & Ziegler, Eva, 2021. "Explaining destinations and volumes of international arms transfers: A novel network Heckman selection model," European Journal of Political Economy, Elsevier, vol. 69(C).
    6. Racek, Daniel & Thurner, Paul W. & Davidson, Brittany I. & Zhu, Xiao Xiang & Kauermann, Göran, 2024. "Conflict forecasting using remote sensing data: An application to the Syrian civil war," International Journal of Forecasting, Elsevier, vol. 40(1), pages 373-391.
    7. Simon Montfort, 2023. "Key predictors for climate policy support and political mobilization: The role of beliefs and preferences," Papers 2306.10144, arXiv.org.
    8. Vestby, Jonas & Buhaug, Halvard & von Uexkull, Nina, 2021. "Why do some poor countries see armed conflict while others do not? A dual sector approach," World Development, Elsevier, vol. 138(C).
    9. 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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