CMAverse: A Suite of Functions for Reproducible Causal Mediation Analyses

Abstract

To the Editor: In this letter, we introduce CMAverse, an R package that provides a suite of functions for reproducible causal mediation analysis including directed acyclic graph (DAG) visualization, statistical modeling for estimation and inference of causal effects, and sensitivity analyses for unmeasured confounding, measurement error and selection bias. In many research fields including biomedical sciences, epidemiology and psychology, the use of causal mediation analysis has been increasing rapidly over the past couple of decades. Mediation analysis can help explain causal relationships and inform policy. In addition to examining a direct causal relation between two variables, mediation models assess causal pathways in which one variable (the exposure) causes another variable (the outcome) through an intermediate variable (the mediator). It has been shown that the overall exposure effect can be decomposed into two components1: a natural direct effect and a natural indirect effect. Additionally, in the presence of exposure–mediator interaction, the overall exposure effect can be further decomposed into four effects1: a controlled direct effect, a reference interaction, a mediated interaction and a pure indirect effect. These decompositions yield valid results under a set of assumptions of no unmeasured confounding. In practice, there might be violations of these such assumptions, which are also untestable. Meanwhile, there might be measurement error and selection bias incu…