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This chapter describes the state of the data on terrorism and isolates some of the key factors associated with the number of terror attacks using a random forest machine learning algorithm. The variables that emerge include several policy outcomes such as health expenditures, anti-government demonstrations, income inequality, democratic accountability, and strikes. Using the example of income inequality we show how model-agnostic machine learning algorithms can uncover nonlinear associations in the determinants and in doing so can improve our measurement and understanding of the nature of causality.
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