论文标题
贝叶斯因果推断在不符合依从性的依次随机实验中
Bayesian Causal Inference in Sequentially Randomized Experiments with Noncompliance
论文作者
论文摘要
科学研究人员利用随机实验来绘制休闲陈述。大多数早期研究以及有关顺序干预决策的实验的当前研究都集中在估计顺序治疗之间的因果关系上，忽略了不合规问题，即实验单位可能不符合最初分配的治疗作业。已经开发了一系列方法来解决随机实验的违规问题，并进行了固定治疗。但是，据我们所知，在顺序实验设置中，几乎没有关于不合规问题的文献研究。在本文中，我们超越了使用每项协议，经过处理或意向性治疗分析的传统方法，并提出了一个潜在的混合物贝叶斯框架，以估计具有不合规问题的顺序实验中的样品平均治疗效果。
Scientific researchers utilize randomized experiments to draw casual statements. Most early studies as well as current work on experiments with sequential intervention decisions has been focusing on estimating the causal effects among sequential treatments, ignoring the non-compliance issues that experimental units might not be compliant with the treatment assignments that they were originally allocated. A series of methodologies have been developed to address the non-compliance issues in randomized experiments with time-fixed treatment. However, to our best knowledge, there is little literature studies on the non-compliance issues in sequential experiments settings. In this paper, we go beyond the traditional methods using per-protocol, as-treated, or intention-to-treat analysis and propose a latent mixture Bayesian framework to estimate the sample-average treatment effect in sequential experiment having non-compliance concerns.
