  In many applied fields, researchers are often interested in tailoring
treatments to unit-level characteristics in order to optimize an outcome of
interest. Methods for identifying and estimating treatment policies are the
subject of the dynamic treatment regime literature. Separately, in many
settings the assumption that data are independent and identically distributed
does not hold due to inter-subject dependence. The phenomenon where a subject's
outcome is dependent on his neighbor's exposure is known as interference. These
areas intersect in myriad real-world settings. In this paper we consider the
problem of identifying optimal treatment policies in the presence of
interference. Using a general representation of interference, via
Lauritzen-Wermuth-Freydenburg chain graphs (Lauritzen and Richardson, 2002), we
formalize a variety of policy interventions under interference and extend
existing identification theory (Tian, 2008; Sherman and Shpitser, 2018).
Finally, we illustrate the efficacy of policy maximization under interference
in a simulation study.
