  Unlike classical causal inference, which often has an average causal effect
of a treatment within a population as a target, in settings such as
personalized medicine, the goal is to map a given unit's characteristics to a
treatment tailored to maximize the expected outcome for that unit. Obtaining
high-quality mappings of this type is the goal of the dynamic regime literature
(Chakraborty and Moodie 2013), with connections to reinforcement learning and
experimental design. Aside from the average treatment effects, mechanisms
behind causal relationships are also of interest. A well-studied approach to
mechanism analysis is establishing average effects along with a particular set
of causal pathways, in the simplest case the direct and indirect effects.
Estimating such effects is the subject of the mediation analysis literature
(Robins and Greenland 1992; Pearl 2001).
  In this paper, we consider how unit characteristics may be used to tailor a
treatment assignment strategy that maximizes a particular path-specific effect.
In healthcare applications, finding such a policy is of interest if, for
instance, we are interested in maximizing the chemical effect of a drug on an
outcome (corresponding to the direct effect), while assuming drug adherence
(corresponding to the indirect effect) is set to some reference level. To solve
our problem, we define counterfactuals associated with path-specific effects of
a policy, give a general identification algorithm for these counterfactuals,
give a proof of completeness, and show how classification algorithms in machine
learning (Chen, Zeng, and Kosorok 2016) may be used to find a high-quality
policy. We validate our approach via a simulation study.
