Unpacking the Black Box of Causality: Learning about Causal Mechanisms from Experimental and Observational Studies
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Author(s)
Yamamoto, Teppei
Imai, Kosuke
Keele, Luke
Tingley, Dustin
Date Issued
November 2011
Journal
American Political Science Review
Publisher
Cambridge University Press
Citation
IMAI, KOSUKE, LUKE KEELE, DUSTIN TINGLEY, and TEPPEI YAMAMOTO. “Unpacking the Black Box of Causality: Learning about Causal Mechanisms from Experimental and Observational Studies.” American Political Science Review 105, no. 04 (November 10, 2011): 765-789. Copyright © American Political Science Association 2011
Version
Final published version
Abstract
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.
MIT Department
Massachusetts Institute of Technology. Department of Political Science
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Article is made available in accordance with the publisher's policy and may be subject to US copyright law. Please refer to the publisher's site for terms of use.
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DOI of Published Version
https://doi.org/10.1017/S0003055411000414
