Disentangling Causal Mechanisms in Conjoint Experiments Using Mediation

2026-07-03

AI Summary

Traditional conjoint experiments cannot identify causal mediation mechanisms among attributes and only estimate controlled direct effects. This study addresses this limitation by introducing an additional, minimally intrusive experimental component that, under standard causal mediation assumptions, enables the first identification and estimation of both natural direct and indirect effects within a conjoint framework. The proposed approach employs a doubly robust estimator augmented with machine learning techniques to enhance model flexibility and estimation accuracy. The method is applied to a pre-registered conjoint experiment on candidate choice, successfully decomposing how specific attributes exert indirect effects through party identification. Empirical results demonstrate the validity and practical utility of the proposed framework for uncovering causal mediation pathways in complex multi-attribute decision settings.

Abstract

Conjoint experiments provide an attractive way to assess the role of multiple attributes simultaneously on decision-making. However, the randomization of multiple attributes prevents understanding the causal mechanisms that, critically, depend on the relationship between attributes -- e.g., how one attribute affects the respondent's belief as to another attribute. This is because conjoint experiments recover controlled effects whereas a substantively important estimand may be the total or indirect effect of one attribute. Unfortunately, existing experimental designs for conjoint experiments cannot estimate these effects. We provide an alternative framework that requires one additional, simple experiment to learn the relationship between attributes among respondents alongside the standard assumptions for causal mediation. Estimation of the relevant effects can be done in a doubly robust fashion using machine learning methods. We illustrate this by conducting a pre-registered experiment on candidate choice and disentangle the effect of different attributes by understanding their mediation through the candidate's party.

Problem

• conjoint experiments
• causal mechanisms
• mediation
• indirect effects
• total effects

Innovation

• causal mediation
• conjoint experiments
• indirect effects
• doubly robust estimation
• attribute interaction