Latently Mediating: A Bayesian Take on Causal Mediation Analysis with Structured Survey Data

• Published in: Multivariate behavioral research Vol. 60; no. 2; pp. 305 - 327
• Main Author: Varacca, Alessandro
• Format: Journal Article
• Language: English
• Published: United States Routledge 04.03.2025; Taylor & Francis Ltd
• Subjects: Algorithms; Bayes Theorem; Bayesian analysis; Bayesian methods; Causal mediation; Causality; Data Interpretation, Statistical; g-computation; Humans; item response theory; latent variables; measurement error; Mediation Analysis; Models, Statistical; Parameter sensitivity; Sensitivity analysis; Surveys and Questionnaires; measurement error; Causal mediation; g-computation; item response theory; Bayesian methods; latent variables
• ISSN: 0027-3171; 1532-7906; 1532-7906
• DOI: 10.1080/00273171.2024.2424514

In this paper, we propose a Bayesian causal mediation approach to the analysis of experimental data when both the outcome and the mediator are measured through structured questionnaires based on Likert-scaled inquiries. Our estimation strategy builds upon the error-in-variables literature and, specifically, it leverages Item Response Theory to explicitly model the observed surrogate mediator and outcome measures. We employ their elicited latent counterparts in a simple g-computation algorithm, where we exploit the fundamental identifying assumptions of causal mediation analysis to impute all the relevant counterfactuals and estimate the causal parameters of interest. We finally devise a sensitivity analysis procedure to test the robustness of the proposed methods to the restrictive requirement of mediator's conditional ignorability. We demonstrate the functioning of our proposed methodology through an empirical application using survey data from an online experiment on food purchasing intentions and the effect of different labeling regimes.