Missing Treatments
分析当个体接受的治疗不完全可观测时（如调查无应答或随机分组不依从），如何识别处理效应。推导出基于缺失治疗分布先验信息的极值边界，并在单调处理反应假设下无需先验信息即可得到有信息量的边界。
This article analyzes the problem of identifying a treatment effect with imperfect observability of the treatment received by the population. Imperfect observability may be due to item/survey nonresponse or to noncompliance with randomly assigned treatments. I derive sharp worst-case bounds that are a function of the available prior information on the distribution of missing treatments. Under the assumption of monotone treatment response, I show that prior information on the distribution of missing treatments is not necessary to get sharp informative bounds. I illustrate the results with an empirical analysis of drug use and employment using data from the National Longitudinal Survey of Youth.