General Identification of Dynamic Treatment Regimes Under Interference


Eli Sherman, David Arbour, Ilya Shpitser ;
Proceedings of the Twenty Third International Conference on Artificial Intelligence and Statistics, PMLR 108:3917-3927, 2020.


In many applied fields, researchers are ofteninterested in tailoring treatments to unit-levelcharacteristics in order to optimize an outcomeof interest. Methods for identifying andestimating treatment policies are the subjectof the dynamic treatment regime literature. Separately, in many settings the assumptionthat data are independent and identically distributeddoes not hold due to inter-subjectdependence. The phenomenon where a subject’s outcome is dependent on his neighbor’s exposure is known as interference. These areasintersect in myriad real-world settings. Inthis paper we consider the problem of identifyingoptimal treatment policies in the presenceof interference. Using a general representationof interference, via Lauritzen-Wermuth-Freydenburg chain graphs (Lauritzen andRichardson, 2002), we formalize a variety ofpolicy interventions under interference andextend existing identification theory (Tian,2008; Sherman and Shpitser, 2018). Finally, we illustrate the efficacy of policy maximization under interference in a simulation study.

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