  Many epidemiological questions concern potential interventions to alter the
pathways presumed to mediate an association. For example, we consider a study
that investigates the benefit of interventions in young adulthood for
ameliorating the poorer mid-life psychosocial outcomes of adolescent
self-harmers relative to their healthy peers. Two methodological challenges
arise. Firstly, mediation methods have hitherto mostly focused on the elusive
task of discovering pathways, rather than on the evaluation of mediator
interventions. Secondly, the complexity of such questions is invariably such
that there are no existing data on well-defined interventions (i.e. actual
treatments, programs, etc.) capturing the populations, outcomes and time-spans
of interest. Instead, researchers must rely on exposure (non-intervention) data
to address these questions, such as self-reported substance use and employment.
We address the resulting challenges by specifying a target trial addressing
three policy-relevant questions, regarding the impacts of hypothetical (rather
than actual) interventions that would shift the mediators' distributions
(separately, jointly or sequentially) to user-specified distributions that can
be emulated with the observed data. We then define novel interventional effects
that map to this trial, emulating shifts by setting mediators to random draws
from those distributions. We show that estimation using a g-computation method
is possible under an expanded set of causal assumptions relative to inference
with well-defined interventions. These expanded assumptions reflect the lower
level of evidence that is inevitable with ill-defined interventions.
Application to the self-harm example using data from the Victorian Adolescent
Health Cohort Study illustrates the value of our proposal for informing the
design and evaluation of actual interventions in the future.
