In randomized experiments with non-compliance scholars have argued that the complier average causal effect (CACE) ought to be the main causal estimand. The literature on inference of the complier average treatment effect (CACE) has focused on inference about the population CACE. However, in general individuals in the experiments are volunteers. This means that there is a risk that individuals partaking in a given experiment differ in important ways from a population of interest. It is thus of interest to focus on the sample at hand and have easy to use and correct procedures for inference about the sample CACE. We consider a more general setting than in the previous literature and construct a confidence interval based on the Wald estimator in the form of a finite closed interval that is familiar to practitioners. Furthermore, with the access of pre-treatment covariates, we propose a new regression adjustment estimator and associated methods for constructing confidence intervals. Finite sample performance of the methods is examined through a Monte Carlo simulation and the methods are used in an application to a job training experiment.

翻译：在存在不依从情况的随机实验中，学者们主张应将依从者平均因果效应（CACE）作为主要因果估计量。关于依从者平均处理效应（CACE）推断的文献主要聚焦于总体CACE的推断。然而，实验中的个体通常是自愿参与者，这意味着参与特定实验的个体可能在重要方面与目标总体存在差异。因此，关注当前样本并开发易于使用且正确的样本CACE推断程序具有重要意义。我们考虑比以往文献更一般的设定，基于Wald估计量构建了实践者熟悉的有限闭区间形式的置信区间。此外，在获取处理前协变量的情况下，我们提出了一种新的回归调整估计量及相应的置信区间构建方法。通过蒙特卡洛模拟考察了方法的有限样本表现，并将该方法应用于一项职业培训实验。