  The incorporation of causal inference in mediation analysis has led to
theoretical and methodological advancements -- effect definitions with causal
interpretation, clarification of assumptions required for effect
identification, and an expanding array of options for effect estimation.
However, the literature on these results is fast-growing and complex, which may
be confusing to researchers unfamiliar with causal inference or unfamiliar with
mediation. The goal of this paper is to help ease the understanding and
adoption of causal mediation analysis. It starts by highlighting a key
difference between the causal inference and traditional approaches to mediation
analysis and making a case for the need for explicit causal thinking and the
causal inference approach in mediation analysis. It then explains in
as-plain-as-possible language existing effect types, paying special attention
to motivating these effects with different types of research questions, and
using concrete examples for illustration. This presentation differentiates two
perspectives (or purposes of analysis): the explanatory perspective (aiming to
explain the total effect) and the interventional perspective (asking questions
about hypothetical interventions on the exposure and mediator, or
hypothetically modified exposures). For the latter perspective, the paper
proposes tapping into a general class of interventional effects that contains
as special cases most of the usual effect types -- interventional direct and
indirect effects, controlled direct effects and also a generalized
interventional direct effect type, as well as the total effect and overall
effect. This general class allows flexible effect definitions which better
match many research questions than the standard interventional direct and
indirect effects.
