Improving Preference Elicitation in Conjoint Designs using Machine Learning for Heterogeneous Effects


Conjoint analysis has become a standard tool for preference elicitation in political science. However the typical estimand, the Average Marginal Component Effect (AMCE), is only tangentially linked to theoretically relevant quantities. In this paper we clarify the necessary theoretical assumptions to interpret the AMCE in terms of individual preferences, explain how heterogeneity in marginal component effects can drive misleading conclusions about preferences, and provide a set of tools based on the causal/generalized random forest method (Athey et al., 2019; Wager & Athey, 2018) that allow applied researchers to detect effect heterogeneity between respondents and derive theoretically relevant quantities of interest from estimates of individual-level marginal component effects. We illustrate this method with an application to a recently conducted conjoint experiment on candidate preferences inthe 2020 U.S. Democratic Presidential primary.