Understanding the Past: Statistical Analysis of Causal Attribution
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Would the third-wave democracies have been democratized without prior modernization? What proportion of the past militarized disputes between nondemocracies would have been prevented had those dyads been democratic? Although political scientists often ask these questions of causal attribution, existing quantitative methods fail to address them. This article proposes an alternative statistical methodology based on the widely accepted counterfactual framework of causal inference. The contribution of this article is threefold. First, it clarifies differences between causal attribution and causal effects by specifying the type of research questions to which each quantity is relevant. Second, it provides a clear resolution of the long-standing methodological debate on “selection on the dependent variable.” Third, the article derives new nonparametric identification results, showing that the complier probability of causal attribution can be identified using an instrumental variable. The proposed framework is illustrated via empirical examples from three subfields of political science.
DepartmentMassachusetts Institute of Technology. Department of Political Science
American Journal of Political Science
Yamamoto, Teppei. “Understanding the Past: Statistical Analysis of Causal Attribution.” American Journal of Political Science 56, no. 1 (January 2012): 237–256.
Author's final manuscript