  Mediation analysis is widely used for investigating direct and indirect
causal pathways through which an effect arises. However, many mediation
analysis studies are challenged by missingness in the mediator and outcome. In
general, when the mediator and outcome are missing not at random, the direct
and indirect effects are not identifiable without further assumptions. In this
work, we study the identifiability of the direct and indirect effects under
some interpretable mechanisms that allow for missing not at random in the
mediator and outcome. We evaluate the performance of statistical inference
under those mechanisms through simulation studies and illustrate the proposed
methods via the National Job Corps Study.
