Causal Effects in Mediation Modeling: An Introduction With Applications to Latent Variables

Abstract

Causal inference in mediation analysis offers counterfactually based causal definitions of direct and indirect effects, drawing on research by Robins, Greenland, Pearl, VanderWeele, Vansteelandt, Imai, and others. This type of mediation effect estimation is little known and seldom used among analysts using structural equation modeling (SEM). The aim of this article is to describe the new analysis opportunities in a way that is accessible to SEM analysts and show examples of how to perform the analyses. An application is presented with an extension to a latent mediator measured with multiple indicators.

Keywords:

• counterfactuals
• randomization
• structural equation modeling

Notes

¹ In the logit case, Mplus carries out numerical integration with 101 integration points.