Estimating Causal Mediation Effects in Multiple-Mediator Analyses with Clustered Data

In multiple-mediator analyses in behavioral sciences, data are often clustered. Despite the growing literature on causal mediation analysis, little research exists on methods for assessing causal mediation effects in multiple-mediator analyses with clustered data, especially when the multiple mediators may be dependent. In this paper, we develop methods for estimating causal mediation effects (particularly the interventional indirect and direct effects) in the multiple-mediator analyses with clustered data, taking into account (possibly) unmeasured cluster-level confounders. Extending the causal mediation literature, we develop three estimators—the regression-based, the weighting-based, and the multiply-robust estimators—for clustered data, with incorporating clusters in estimating the models involved. The simulation results show that our developed methods (including the three estimators with multilevel random-effects or fixed-effects models, and the multiply-robust estimator with machine learning methods to estimate the models incorporating clusters) can yield satisfactory estimates of the causal mediation effects for the multiple-mediator analysis with clustered data. We illustrate our methods in an example using data from the Educational Longitudinal Study.