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General Identification of Dynamic Treatment Regimes Under Interference
arXiv - CS - Artificial Intelligence Pub Date : 2020-04-02 , DOI: arxiv-2004.01218
Eli Sherman, David Arbour, Ilya Shpitser

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.

中文翻译:

干扰下动态处理制度的一般识别

在许多应用领域,研究人员通常对根据单元级特征定制治疗方法感兴趣,以优化感兴趣的结果。确定和估计治疗政策的方法是动态治疗方案文献的主题。另外,在许多情况下,由于主体间的依赖性,数据独立且同分布的假设不成立。对象的结果取决于其邻居的曝光的现象称为干扰。这些领域在无数的现实世界环境中相交。在本文中,我们考虑在存在干扰的情况下确定最佳治疗策略的问题。使用干扰的一般表示,通过 Lauritzen-Wermuth-Freydenburg 链图(Lauritzen 和 Richardson,2002),我们在干扰下将各种政策干预形式化,并扩展了现有的认同理论(Tian,2008 年;Sherman 和 Shpitser,2018 年)。最后,我们在模拟研究中说明了在干扰下策略最大化的功效。
更新日期:2020-04-06
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