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date: 27 May 2019

Abstract and Keywords

There is a rich legacy of quantitative work in Security Studies, with scholars using regression to make a variety of discoveries about questions of interest. Unfortunately, much of this work pays scant attention to the differences among description, causation, and prediction. This chapter draws on existing work in political science, economics, and statistics to illustrate the distinctions among these approaches and the models and assumptions appropriate for each. The chapter closes with the hope that better quantitative research will lead to improvements in the field of international security and bring everyday methods more in line with the traditions of strong theorizing and effective data-gathering. It also provides resources for the reader to further explore the ideas presented in the chapter.

Keywords: prediction, causality, causal inference, machine learning, potential outcomes, security studies

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