  This paper aims to contribute to helping practitioners of causal mediation
analysis gain a better understanding of estimation options. We take as inputs
two familiar strategies (weighting and model-based prediction) and a simple way
of combining them (weighted models), and show how we can generate a range of
estimators with different modeling requirements and robustness properties. The
primary goal is to help build intuitive appreciation for robust estimation that
is conducive to sound practice that does not require advanced statistical
knowledge. A second goal is to provide a "menu" of estimators that
practitioners can choose from for the estimation of marginal natural (in)direct
effects. The estimators generated from this exercise include some that coincide
or are similar to existing estimators and others that have not appeared in the
literature. We use a random continuous weights bootstrap to obtain confidence
intervals, and also derive general asymptotic (sandwich) variance formulas for
the estimators. The estimators are illustrated using data from an adolescent
alcohol use prevention study.
