Replication Data for: Profiling Compliers and Non-compliers for Instrumental-Variable Analysis

Instrumental-variable (IV) estimation is an essential method for applied researchers across the social and behavioral sciences who analyze randomized control trials marred by non-compliance or leverage partially exogenous treatment variation in observational studies. The potential outcomes framework is a popular model to motivate the assumptions underlying the identification of the local average treatment effect (LATE), and to stratify the sample into compliers, always-takers, and never-takers. However, applied research has thus far paid little attention to the characteristics of compliers and non-compliers. Yet profiling compliers and non-compliers is necessary to understand what subpopulation the researcher is making inferences about, and an important first step in evaluating the external validity (or lack thereof) of the LATE estimated for compliers. In this letter, we discuss the assumptions necessary for profiling, which are weaker than the assumptions necessary for identifying the LATE if the instrument is randomly assigned. We introduce a simple and general method to characterize compliers, always-takers and never-takers in terms of their covariates, and easy-to-use software in R and STATA that implements our estimator. We hope that our method and software facilitate the profiling of compliers and non-compliers as standard practice accompanying any IV analysis.

工具变量（Instrumental-variable, IV）估计是社会科学与行为科学领域应用研究者的核心分析方法，这类研究者常针对存在不依从问题的随机对照试验展开分析，或在观察性研究中借助部分外生的处理变量变异开展研究。潜在结果框架（Potential Outcomes Framework）是一种常用模型，既可用于阐释局部平均处理效应（Local Average Treatment Effect, LATE）识别所需的核心假设，也可将研究样本划分为依从者、总是接受者与从不接受者三类。但迄今为止，应用研究领域对依从者与非依从者的特征刻画关注度较低。然而，对依从者与非依从者进行特征刻画，是明确研究者的统计推论所针对的亚群体的必要前提，同时也是评估针对依从者估计得到的LATE的外部有效性（或缺乏有效性）的重要第一步。在本短讯中，我们将讨论特征刻画所需的假设前提——若工具变量为随机分配，则该假设弱于识别LATE所需的假设。我们提出了一种简洁通用的方法，可基于协变量特征对依从者、总是接受者与从不接受者进行刻画，并提供了可直接使用的R与STATA软件以实现我们的估计量。我们期望，本方法与软件能够推动将依从者与非依从者的特征刻画作为所有工具变量分析的标准配套步骤。