论文标题
干扰下动态治疗方案的一般识别
General Identification of Dynamic Treatment Regimes Under Interference
论文作者
论文摘要
在许多应用领域中，研究人员通常有兴趣定制单位级特征的治疗方法，以优化感兴趣的结果。识别和估计治疗政策的方法是动态治疗方案文献的主题。另外，在许多设置中，由于受试者间的依赖性，数据是独立且分布式相同的假设。受试者的结果取决于邻居的暴露的现象被称为干扰。这些领域在无数现实的环境中相交。在本文中，我们考虑了在干扰存在下确定最佳治疗政策的问题。使用劳动津 - 弗雷德堡链链图（Lauritzen and Richardson，2002），使用干扰的一般表示，我们正式化了在干扰下进行的各种政策干预措施并扩展了现有的识别理论（Tian，2008; Sherman and Shpitser，2018）。最后，我们说明了在仿真研究中干扰政策最大化的功效。
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.
