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Why Process Matters for Causal Inference

Published online by Cambridge University Press:  04 January 2017

Adam N. Glynn  and
Kevin M. Quinn
Adam N. Glynn*
Affiliation:
Department of Government, Harvard University, 1737 Cambridge Street, Cambridge, MA 02138
Kevin M. Quinn
Affiliation:
UC Berkeley School of Law, 490 Simon 7200, Berkeley, CA 94720-7200. e-mail: kquinn@law.berkeley.edu
*
e-mail: aglynn@fas.harvard.edu (corresponding author)
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Abstract

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Our goal in this paper is to provide a formal explanation for how within-unit causal process information (i.e., data on posttreatment variables and partial information on posttreatment counterfactuals) can help to inform causal inferences relating to total effects—the overall effect of an explanatory variable on an outcome variable. The basic idea is that, in many applications, researchers may be able to make more plausible causal assumptions conditional on the value of a posttreatment variable than they would be able to do unconditionally. As data become available on a posttreatment variable, these conditional causal assumptions become active and information about the effect of interest is gained. This approach is most beneficial in situations where it is implausible to assume that treatment assignment is conditionally ignorable. We illustrate the approach with an example of estimating the effect of election day registration on turnout.

Information

Type
Research Article
Information
Political Analysis , Volume 19 , Issue 3 , Summer 2011 , pp. 273 - 286
Copyright
Copyright © The Author 2011. Published by Oxford University Press on behalf of the Society for Political Methodology 

Footnotes

Authors' note: The authors thank Matt Chingos for his research assistance and Kevin Clarke, Luke Keele, Jay Kaufman, James Mahoney, the participants of the 2009 meeting of the Midwest Political Science Association, the participants of the 2009 Causal Workshop at the Banff International Research Station, the participants of the 2009 Summer meeting of the Society of Political Methodology, two anonymous referees, and the editors for their helpful comments and suggestions.

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