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

Abstract

Multiple-mediator analyses with clustered data are common in educational and behavioral sciences, but limited methods exist to assess the causal mediation effects via each of multiple mediators. In this study, we extend the multiply robust method to make inferences on the causal mediation effects for two mediators with clustered data. The developed method takes into account unmeasured cluster-level confounders and can incorporate machine learning methods to nonparametrically estimate nuisance models while allowing uncertainty quantification via asymptotic standard errors and confidence intervals. We conduct simulations to evaluate the developed method for inference of both the individual-average and cluster-average causal mediation effects with clustered data. We illustrate our method using data from the Education Longitudinal Study.