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Causal Inference in Conjoint Analysis: Understanding Multidimensional Choices via Stated Preference Experiments

Published online by Cambridge University Press:  04 January 2017

Jens Hainmueller
Affiliation:
Department of Political Science, Massachusetts Institute of Technology, Cambridge, MA 02139 e-mail: jhainm@mit.edu
Daniel J. Hopkins
Affiliation:
Department of Government, Georgetown University, ICC 681, Washington, DC 20057 e-mail: dh335@georgetown.edu
Teppei Yamamoto*
Affiliation:
Department of Political Science, Massachusetts Institute of Technology, 77 Massachusetts Avenue, Cambridge, MA 02139
*
e-mail: teppei@mit.edu (corresponding author)
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Abstract

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Survey experiments are a core tool for causal inference. Yet, the design of classical survey experiments prevents them from identifying which components of a multidimensional treatment are influential. Here, we show how conjoint analysis, an experimental design yet to be widely applied in political science, enables researchers to estimate the causal effects of multiple treatment components and assess several causal hypotheses simultaneously. In conjoint analysis, respondents score a set of alternatives, where each has randomly varied attributes. Here, we undertake a formal identification analysis to integrate conjoint analysis with the potential outcomes framework for causal inference. We propose a new causal estimand and show that it can be nonparametrically identified and easily estimated from conjoint data using a fully randomized design. The analysis enables us to propose diagnostic checks for the identification assumptions. We then demonstrate the value of these techniques through empirical applications to voter decision making and attitudes toward immigrants.

Type
Research Article
Copyright
Copyright © The Author 2013. Published by Oxford University Press on behalf of the Society for Political Methodology 

Footnotes

Authors' note: We gratefully acknowledge the recommendations of Political Analysis editors Michael Alvarez and Jonathan Katz as well as the anonymous reviewers. We further thank Justin Grimmer, Kosuke Imai, and seminar participants at MIT, Harvard University, Georgetown University, and Rochester University for their helpful comments and suggestions. We are also grateful to Anton Strezhnev for excellent research assistance. An earlier version of this article was presented at the 2012 Annual Summer Meeting of the Society for Political Methodology and the 2013 Annual Meeting of the American Political Science Association. Example scripts that illustrate the estimators and companion software to embed a conjoint analysis in Web-based survey instruments are available on the authors' websites. Replication materials are available online as Hainmueller, Hopkins, and Yamamoto (2013). Supplementary materials for this article are available on the Political Analysis Web site.

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