中文摘要
多值处理在实际应用中十分常见。我们探讨在此情境下使用取离散值的工具变量来控制选择性偏误。我们的讨论围绕“靶向”这一概念展开:哪些工具变量靶向哪些处理。这一概念使我们能够确立如下条件:在这些条件下,反事实均值与处理效应对于复合依从组而言是点识别或部分识别的。我们考察了正选择假设所带来的额外识别能力。我们通过重新审视 Kline 与 Walters (2016) 关于“开端计划”影响研究(Head Start Impact Study)的发现来说明其有用性。我们推导出具有信息量的界限,这些界限表明“开端计划”扩张的有益效应低于其参数估计所隐含的水平。
Abstract
Multivalued treatments are commonplace in applications. We explore the use of discrete-valued instruments to control for selection bias in this setting. Our discussion revolves around the concept of targeting: which instruments target which treatments. It allows us to establish conditions under which counterfactual averages and treatment effects are point- or partially-identified for composite complier groups. We explore the additional identifying power of a positive selection assumption. We illustrate its usefulness by revisiting the findings of Kline and Walters (2016) on the Head Start Impact Study. We derive informative bounds that suggest less beneficial effects of Head Start expansions than their parametric estimates.