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

Jeroen Mulder*, Kim Luijken, Bas Penning de Vries, Ellen Hamaker

*Corresponding author for this work

Research output: Contribution to journalArticleAcademicpeer-review

Abstract

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.
Original languageEnglish
Pages (from-to)575-591
Number of pages17
JournalStructural Equation Modeling: A Multidisciplinary Journal
Volume31
Issue number4
Early online date19 Mar 2024
DOIs
Publication statusPublished - Jul 2024

Keywords

  • Inverse probability weighting
  • marginal structural models
  • observational data
  • structural equation modeling
  • time-varying causal effect

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