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Improving Supreme Court Forecasting Using Boosted Decision Trees

  • Aaron Russell Kaufman (a1), Peter Kraft (a2) and Maya Sen (a3)

Abstract

Though used frequently in machine learning, boosted decision trees are largely unused in political science, despite many useful properties. We explain how to use one variant of boosted decision trees, AdaBoosted decision trees (ADTs), for social science predictions. We illustrate their use by examining a well-known political prediction problem, predicting U.S. Supreme Court rulings. We find that our ADT approach outperforms existing predictive models. We also provide two additional examples of the approach, one predicting the onset of civil wars and the other predicting county-level vote shares in U.S. presidential elections.

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Copyright

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Footnotes

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Authors’ note: Replication materials available at the Political Analysis Dataverse: https://doi.org/10.7910/DVN/JJCXTH (Kaufman, Kraft and Sen 2018)

Contributing Editor: Jeff Gill

Footnotes

References

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Bansak, K., Ferwerda, J., Hainmueller, J., Dillon, A., Hangartner, D., Lawrence, D., and Weinstein, J.. 2018. “Improving Refugee Integration through Data-Driven Algorithmic Assignment.” Science 359(6373):325329.
Black, R. C., Treul, S. A., Johnson, T. R., and Goldman, J.. 2011. “Emotions, Oral Arguments, and Supreme Court Decision Making.” The Journal of Politics 73(2):572581.
Collier, P., and Hoeffler, A.. 2002. “On the Incidence of Civil War in Africa.” Journal of Conflict Resolution 46(1):1328.
Efron, B., and Tibshirani, R.. 1997. “Improvements on Cross-Validation: the $632+$ Bootstrap Method.” Journal of the American Statistical Association 92(438):548560.
Elith, J., Leathwick, J. R., and Hastie, T.. 2008. “A Working Guide to Boosted Regression Trees.” Journal of Animal Ecology 77(4):802813.
Epstein, L., Landes, W. M., and Posner, R. A.. 2010. “Inferring the Winning Party in the Supreme Court from the Pattern of Questioning at Oral Argument.” The Journal of Legal Studies 39(2):433467.
Fearon, J. D., and Laitin, D. D.. 2003. “Ethnicity, Insurgency, and Civil War.” American Political Science Review 97(1):7590.
Freund, Y., and Schapire, R. E.. 1996. “Experiments with a New Boosting Algorithm.” In Proceedings of the Thirteenth International Conference on Machine Learning, vol. 96 , 148156. San Francisco, CA: Morgan Kaufmann Publishers.
Gelman, A., and King, G.. 1993. “Why are American Presidential Election Campaign Polls so Variable when Votes are so Predictable? British Journal of Political Science 23(4):409451.
Goldman, J.2002. The OYEZ Project [On-line].
Green, D. P., and Kern, H. L.. 2012. “Modeling Heterogeneous Treatment Effects in Survey Experiments with Bayesian Additive Regression Trees.” Public Opinion Quarterly 76(3):491511.
Hibbs, D. A. Jr. 2000. “Bread and Peace Voting in US Presidential Elections.” Public Choice 104(1–2):149180.
Johnson, T. R., Wahlbeck, P. J., and Spriggs, J. F.. 2006. “The Influence of Oral Arguments on the US Supreme Court.” American Political Science Review 100(1):99113.
Katz, D. M., Bommarito, M. J., and Blackman, J.. 2014. “Predicting the Behavior of the Supreme Court of the United States: A General Approach.” Available at SSRN: http://dx.doi.org/10.2139/ssrn.2463244.
Katz, D. M., Bommarito, M. J. II, and Blackman, J.. 2017. “A General Approach for Predicting the Behavior of the Supreme Court of the United States.” PloS one 12(4): e0174698.
Kaufman, A., Kraft, P., and Sen, M.. 2018. “Replication Data for: Improving Supreme Court Forecasting Using Boosted Decision Trees.” https://doi.org/10.7910/DVN/JJCXTH, Harvard Dataverse, V1.
Martin, A. D., Quinn, K. M., Ruger, T. W., and Kim, P. T.. 2004. “Competing Approaches to Predicting Supreme Court Decision Making.” Perspectives on Politics 2(4):761767.
Montgomery, J. M., and Olivella, S.. 2016. “Tree-Based Models for Political Science Data.” American Journal of Political Science 62(3):729744.
Muchlinski, D., Siroky, D., He, J., and Kocher, M.. 2016. “Comparing Random Forest with Logistic Regression for Predicting Class-Imbalanced Civil War Onset Data.” Political Analysis 24(1):87103.
Mukherjee, I., Rudin, C., and Schapire, R. E.. 2011. “The Rate of Convergence of Adaboost.” In Proceedings of the 24th Annual Conference on Learning Theory , 537558. Association for Computational Learning.
Ruger, T. W., Kim, P. T., Martin, A. D., and Quinn, K. M.. 2004. “The Supreme Court Forecasting Project: Legal and Political Science Approaches to Predicting Supreme Court Decisionmaking.” Columbia Law Review 104:11501210.
Spaeth, H. J., Epstein, L., Martin, A. D., Segal, J. A., Ruger, T. J., and Benesh, S. C.. 2015. The Supreme Court database . Center for Empirical Research in the Law at Washington University.
Ward, M. D., Greenhill, B. D., and Bakke, K. M.. 2010. “The Perils of Policy by p-value: Predicting Civil Conflicts.” Journal of Peace Research 47(4):363375.
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Keywords

Type Description Title
UNKNOWN
Supplementary materials

Kaufman et al. dataset
Dataset

 Unknown
UNKNOWN
Supplementary materials

Kaufman et al. supplementary material
Kaufman et al. supplementary material 1

 Unknown (432 KB)
432 KB

Improving Supreme Court Forecasting Using Boosted Decision Trees

  • Aaron Russell Kaufman (a1), Peter Kraft (a2) and Maya Sen (a3)

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