  The goal of personalized decision making is to map a unit's characteristics
to an action tailored to maximize the expected outcome for that unit. Obtaining
high-quality mappings of this type is the goal of the dynamic regime
literature. In healthcare settings, optimizing policies with respect to a
particular causal pathway may be of interest as well. For example, we may wish
to maximize the chemical effect of a drug given data from an observational
study where the chemical effect of the drug on the outcome is entangled with
the indirect effect mediated by differential adherence. In such cases, we may
wish to optimize the direct effect of a drug, while keeping the indirect effect
to that of some reference treatment. [16] shows how to combine mediation
analysis and dynamic treatment regime ideas to defines policies associated with
causal pathways and counterfactual responses to these policies. In this paper,
we derive a variety of methods for learning high quality policies of this type
from data, in a causal model corresponding to a longitudinal setting of
practical importance. We illustrate our methods via a dataset of HIV patients
undergoing therapy, gathered in the Nigerian PEPFAR program.
