General Identification of Dynamic Treatment Regimes Under Interference

TL;DR

This paper develops a framework for identifying optimal dynamic treatment policies in settings where interference between units exists, extending existing causal inference theories and demonstrating effectiveness through simulations.

Contribution

It introduces a general approach using chain graphs to formalize and identify treatment policies under interference, expanding prior identification methods.

Findings

Effective policy maximization demonstrated in simulations

Extended identification theory to account for interference

Formalized treatment interventions using chain graphs

Abstract

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.