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.

Abstract

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.

Cite this Paper


BibTeX
@InProceedings{pmlr-v108-sherman20a, title = {General Identification of Dynamic Treatment Regimes Under Interference}, author = {Sherman, Eli and Arbour, David and Shpitser, Ilya}, booktitle = {Proceedings of the Twenty Third International Conference on Artificial Intelligence and Statistics}, pages = {3917--3927}, year = {2020}, editor = {Silvia Chiappa and Roberto Calandra}, volume = {108}, series = {Proceedings of Machine Learning Research}, month = {26--28 Aug}, publisher = {PMLR}, pdf = {http://proceedings.mlr.press/v108/sherman20a/sherman20a.pdf}, url = { http://proceedings.mlr.press/v108/sherman20a.html }, abstract = {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.} }
Endnote
%0 Conference Paper %T General Identification of Dynamic Treatment Regimes Under Interference %A Eli Sherman %A David Arbour %A Ilya Shpitser %B Proceedings of the Twenty Third International Conference on Artificial Intelligence and Statistics %C Proceedings of Machine Learning Research %D 2020 %E Silvia Chiappa %E Roberto Calandra %F pmlr-v108-sherman20a %I PMLR %P 3917--3927 %U http://proceedings.mlr.press/v108/sherman20a.html %V 108 %X 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.
APA
Sherman, E., Arbour, D. & Shpitser, I.. (2020). General Identification of Dynamic Treatment Regimes Under Interference. Proceedings of the Twenty Third International Conference on Artificial Intelligence and Statistics, in Proceedings of Machine Learning Research 108:3917-3927 Available from http://proceedings.mlr.press/v108/sherman20a.html .

Related Material