Experimental designs for identifying causal mechanisms
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Yamamoto_Experimental designs.pdf
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Author(s)
Imai, Kosuke
Tingley, Dustin
Yamamoto, Teppei
Date Issued
November 2012
Journal
Journal of the Royal Statistical Society: Series A (Statistics in Society)
Publisher
Wiley Blackwell
Citation
Imai, Kosuke, Dustin Tingley, and Teppei Yamamoto. “Experimental Designs for Identifying Causal Mechanisms.” Journal of the Royal Statistical Society: Series A (Statistics in Society) 176, no. 1 (January 2013): 5–51.
Version
Author's final manuscript
Abstract
Experimentation is a powerful methodology that enables scientists to establish causal claims empirically. However, one important criticism is that experiments merely provide a black box view of causality and fail to identify causal mechanisms. Specifically, critics argue that, although experiments can identify average causal effects, they cannot explain the process through which such effects come about. If true, this represents a serious limitation of experimentation, especially for social and medical science research that strives to identify causal mechanisms. We consider several experimental designs that help to identify average natural indirect effects. Some of these designs require the perfect manipulation of an intermediate variable, whereas others can be used even when only imperfect manipulation is possible. We use recent social science experiments to illustrate the key ideas that underlie each of the designs proposed.
MIT Department
Massachusetts Institute of Technology. Department of Political Science
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Creative Commons Attribution-Noncommercial-Share Alike
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DOI of Published Version
https://doi.org/10.1111/j.1467-985X.2012.01032.x
