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.
翻译:科学研究人员利用随机实验得出临时说明。大多数早期研究和目前关于连续干预决定的实验工作一直侧重于估计先后处理方法的因果关系,忽视了实验单位可能不符合最初分配的治疗任务的不遵约问题,制定了一系列方法来解决随机实验中的不遵约问题,同时进行时间固定处理。然而,据我们所知,关于连续试验环境中的不遵约问题的文献研究很少。在本文中,我们超越了使用经处理的/protocol或意图-处理分析的传统方法,并提出了一个潜在的Bayesian混合物框架,以估计有不遵守问题的连续实验中样本-平均处理效果。