Formal Models, Causal Inference, and American Political Development

Sean Gailmard ()
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Sean Gailmard: Department of Political Science, University of California

A chapter in Causal Inference and American Political Development, 2024, pp 99-123 from Springer

Abstract: Abstract I explore the interconnections between formal models, causal inference, and American political development. I focus on formal models based on game theory and argue that, despite many years of mutual critique, game-theoretic models and APD share important methodological and substantive overlap. As a result, they have no inherent antagonism and, in some ways, can be mutually beneficial. For causal inference in APD, I argue that formal models are useful for two reasons: first to depict generative causal mechanisms that can explain the causal effects identified with standard empirical techniques; second, to show connections between packages of variables and lend credibility to causal arguments when strong identification is not otherwise possible. I examine these claims in light of examples from the literature.

Keywords: Game theory; American political development; Historical political economy (search for similar items in EconPapers)
Date: 2024
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Persistent link: https://EconPapers.repec.org/RePEc:spr:stpchp:978-3-031-74913-1_6

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DOI: 10.1007/978-3-031-74913-1_6

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