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Unpacking the Black Box of Causality: Learning about Causal Mechanisms from Experimental and Observational Studies


Identifying causal mechanisms is a fundamental goal of social science. Researchers seek to study not only whether one variable affects another but also how such a causal relationship arises. Yet commonly used statistical methods for identifying causal mechanisms rely upon untestable assumptions and are often inappropriate even under those assumptions. Randomizing treatment and intermediate variables is also insufficient. Despite these difficulties, the study of causal mechanisms is too important to abandon. We make three contributions to improve research on causal mechanisms. First, we present a minimum set of assumptions required under standard designs of experimental and observational studies and develop a general algorithm for estimating causal mediation effects. Second, we provide a method for assessing the sensitivity of conclusions to potential violations of a key assumption. Third, we offer alternative research designs for identifying causal mechanisms under weaker assumptions. The proposed approach is illustrated using media framing experiments and incumbency advantage studies.

Corresponding author
Kosuke Imai is Associate Professor, Department of Politics, Princeton University, Corwin Hall 036, Princeton NJ 08544 (
Luke Keele is Assistant Professor, Department of Political Science, Pennsylvania State University, 211 Pond Lab, University Park, PA 16802 (
Dustin Tingley is Assistant Professor, Department of Government, Harvard University, 1737 Cambridge Street, CGIS Knafel Building 208, Cambridge MA 02138 (
Teppei Yamamoto is Assistant Professor, Department of Political Science, Massachusetts Institute of Technology, 77 Massachusetts Avenue, Cambridge, MA 02139 (
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American Political Science Review
  • ISSN: 0003-0554
  • EISSN: 1537-5943
  • URL: /core/journals/american-political-science-review
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