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The Statistics of Causal Inference: A View from Political Methodology

  • Luke Keele (a1)

Many areas of political science focus on causal questions. Evidence from statistical analyses is often used to make the case for causal relationships. While statistical analyses can help establish causal relationships, it can also provide strong evidence of causality where none exists. In this essay, I provide an overview of the statistics of causal inference. Instead of focusing on specific statistical methods, such as matching, I focus more on the assumptions needed to give statistical estimates a causal interpretation. Such assumptions are often referred to as identification assumptions, and these assumptions are critical to any statistical analysis about causal effects. I outline a wide range of identification assumptions and highlight the design-based approach to causal inference. I conclude with an overview of statistical methods that are frequently used for causal inference.

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Authors' note: For comments I thank the editors and the four anonymous reviewers. I also thank Rocío Titiunik, Jasjeet Sekhon, Paul Rosenbaum, and Dylan Small for many insightful conversations about these topics over the years. In the online Supplementary Materials, I provide further information about software tools to implement many of the methodologies discussed in this essay. Supplementary materials for this article are available on the Political Analysis Web site.

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Political Analysis
  • ISSN: 1047-1987
  • EISSN: 1476-4989
  • URL: /core/journals/political-analysis
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Supplementary materials

Keele supplementary material
Supplementary Material

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