  Recent approaches to causal inference have focused on causal effects defined
as contrasts between the distribution of counterfactual outcomes under
hypothetical interventions on the nodes of a graphical model. In this article
we develop theory for causal effects defined with respect to a different type
of intervention, one which alters the information propagated through the edges
of the graph. These information transfer interventions may be more useful than
node interventions in settings in which causes are non-manipulable, for example
when considering race or genetics as a causal agent. Furthermore, information
transfer interventions allow us to define path-specific decompositions which
are identified in the presence of treatment-induced mediator-outcome
confounding, a practical problem whose general solution remains elusive. We
prove that the proposed effects provide valid statistical tests of mechanisms,
unlike popular methods based on randomized interventions on the mediator. We
propose efficient non-parametric estimators for a covariance version of the
proposed effects, using data-adaptive regression coupled with semi-parametric
efficiency theory to address model misspecification bias while retaining
$\sqrt{n}$-consistency and asymptotic normality. We illustrate the use of our
methods in two examples using publicly available data.
