General Identification of Dynamic Treatment Regimes Under Interference
This work addresses the challenge of tailoring treatments in settings with inter-subject dependence, which is common in fields like public health or social sciences, but it is incremental as it builds on prior identification theory.
The paper tackles the problem of identifying optimal dynamic treatment regimes when interference occurs, meaning a subject's outcome depends on neighbors' treatments, by extending existing identification theory using chain graphs and demonstrating efficacy in simulations.
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.