Causal Effects of Time-Varying Exposures: A Comparison of Structural Equation Modeling and Marginal Structural Models in Cross-Lagged Panel Research

• Published in: Structural equation modeling Vol. 31; no. 4; pp. 575 - 591
• Main Authors: Mulder, Jeroen D., Luijken, Kim, Penning de Vries, Bas B. L., Hamaker, Ellen L.
• Format: Journal Article
• Language: English
• Published: Hove Routledge 03.07.2024; Psychology Press
• Subjects: Bias; Causality; Inverse probability weighting; marginal structural model; observational data; Parametric statistics; Simulation; structural equation modeling; time-varying causal effect
• ISSN: 1070-5511; 1532-8007
• DOI: 10.1080/10705511.2024.2316586

The use of structural equation models for causal inference from panel data is critiqued in the causal inference literature for unnecessarily relying on a large number of parametric assumptions, and alternative methods originating from the potential outcomes framework have been recommended, such as inverse probability weighting (IPW) estimation of marginal structural models (MSMs). To better understand this criticism, we describe three phases of causal research. We explain (differences in) the assumptions that are made throughout these phases for structural equation modeling (SEM) and IPW-MSM approaches using an empirical example. Second, using simulations we compare the finite sample performance of SEM and IPW-MSM for the estimation of time-varying exposure effects on an end-of-study outcome under violations of parametric assumptions. Although increased reliance on parametric assumptions does not always translate to increased bias (even under model misspecification), researchers are still well-advised to acquaint themselves with causal methods from the potential outcomes framework.