Replication Data for: \"Estimating the Local Average Treatment Effect Without the Exclusion Restriction\"

Existing approaches to conducting inference about the Local Average Treatment Effect or LATE require assumptions that are considered tenuous in many applied settings. In particular, Instrumental Variable techniques require monotonicity and the exclusion restriction while principal score methods rest on some form of the principal ignorability assumption. This paper provides new results showing that an estimator within the class of principal score methods allows conservative inference about the LATE without invoking such assumptions. I term this estimator the Compliance Probability Weighting (CPW) estimator and show that, under very mild assumptions, it provides an asymptotically conservative estimator for the LATE. I apply this estimator to a recent survey experiment and provide evidence of a stronger effect for the subset of compliers than the original authors had uncovered. Note: Code replicates the results in the manuscript to approximation error现有针对局部平均处理效应（Local Average Treatment Effect，简称LATE）开展推断的方法，在诸多实际应用场景中所需的假设往往被认为难以成立。具体而言，工具变量（Instrumental Variable）法需要满足单调性与排他性约束，而主得分方法则依赖于某种形式的主可忽略性假设。本文提出全新研究结论，证明主得分方法类中的某一估计量无需引入上述假设，即可实现针对LATE的保守推断。本文将该估计量命名为依从概率加权（Compliance Probability Weighting，简称CPW）估计量，并证明在极弱的假设条件下，该估计量可针对LATE得到渐近保守的估计结果。本文将该估计量应用于一项近期的调查实验，结果表明，相较于原研究作者的发现，依从者子样本的处理效应更为显著。注：本代码可复现论文手稿中的结果，误差仅来自近似计算