Causal Inference in Conjoint Analysis: Understanding Multidimensional Choices via Stated Preference Experiments
Stanford University - Department of Political Science; Stanford Graduate School of Business
Daniel J. Hopkins
Massachusetts Institute of Technology (MIT) - Department of Political Science
November 5, 2013
Political Analysis (Winter 2014) 22 (1): 1-30
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. We then demonstrate the value of these techniques through empirical applications to voter decision-making and attitudes toward immigrants.
Keywords: potential outcomes, average marginal component effects, conjoint analysis, survey experiments, public opinion, vote choice, immigration
JEL Classification: C35, C42, M3, C8, C9
Date posted: March 11, 2013 ; Last revised: January 29, 2014
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