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

TLDR

Survey experiments are a core tool for causal inference, but their design prevents identification of which components of a multidimensional treatment are influential. The study demonstrates that conjoint analysis can estimate causal effects of multiple treatment components and test several causal hypotheses, by integrating it with the potential outcomes framework, proposing a new causal estimand that is nonparametrically identified and easily estimated from fully randomized conjoint data. Conjoint analysis scores alternatives with randomly varied attributes, enabling estimation of causal effects of multiple treatment components. The approach allows diagnostic checks of identification assumptions and demonstrates value through empirical studies on voter decision making and attitudes toward immigrants.

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.