A computational approach to identification of treatment effects for policy evaluation
提出一个计算框架，在工具变量仅为二元时，系统计算政策相关处理参数的尖锐非参数边界，用于外推局部处理效应到反事实情境，并应用于医疗保险政策对医疗服务使用的影响分析。
For counterfactual policy evaluation, it is important to ensure that treatment parameters are relevant to policies in question. This is especially challenging under unobserved heterogeneity, as is well featured in the definition of the local average treatment effect (LATE). Being intrinsically local, the LATE is known to lack external validity in counterfactual environments. This paper investigates the possibility of extrapolating local treatment effects to different counterfactual settings when instrumental variables can be only binary. We propose a novel framework to systematically calculate sharp nonparametric bounds on various policy-relevant treatment parameters that are defined as weighted averages of the marginal treatment effect (MTE). Our framework is flexible enough to fully incorporate statistical independence (rather than mean independence) of instruments and a large menu of identifying assumptions beyond the shape restrictions on the MTE that have been considered in prior studies. We apply our method to understand the effects of medical insurance policies on the use of medical services.