Testing the Unconfoundedness Assumption via Inverse Probability Weighted Estimators of (L)ATT
提出逆概率加权估计量估计局部平均处理效应，并基于单侧不依从性构建检验处理分配是否无混杂的统计检验，蒙特卡洛模拟显示无混杂性带来的效率提升在预检验后仍部分保留。
We propose inverse probability weighted estimators for the local average treatment effect (LATE) and the local average treatment effect for the treated (LATT) under instrumental variable assumptions with covariates. We show that these estimators are asymptotically normal and efficient. When the (binary) instrument satisfies one-sided noncompliance, we propose a Durbin–Wu–Hausman-type test of whether treatment assignment is unconfounded conditional on some observables. The test is based on the fact that under one-sided noncompliance LATT coincides with the average treatment effect for the treated (ATT). We conduct Monte Carlo simulations to demonstrate, among other things, that part of the theoretical efficiency gain afforded by unconfoundedness in estimating ATT survives pretesting. We illustrate the implementation of the test on data from training programs administered under the Job Training Partnership Act in the United States. This article has online supplementary material.