Causal Inference in Conjoint Analysis: Understanding Multidimensional Choices via Stated Preference Experiments
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Yamamoto_Causal inference.pdf
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Author(s)
Hainmueller, Jens
Yamamoto, Teppei
Hopkins, Daniel J.
Date Issued
December 2013
Journal
Political Analysis
Publisher
Oxford University Press
Citation
Hainmueller, J., D. J. Hopkins, and T. Yamamoto. “Causal Inference in Conjoint Analysis: Understanding Multidimensional Choices via Stated Preference Experiments.” Political Analysis (December 19, 2013).
Version
Author's final manuscript
Abstract
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.
MIT Department
Massachusetts Institute of Technology. Department of Political Science
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Creative Commons Attribution-Noncommercial-Share Alike 3.0
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DOI of Published Version
https://doi.org/10.1093/pan/mpt024
