中文摘要
工具变量（IV）在经济学中被广泛用于处理基于不可观测因素的选择问题。标准工具变量方法得到的因果效应估计仅针对那些行为能够被所用工具变量改变的个体。在许多情况下，这些个体与那些会因研究者关注的某项干预或政策而接受处理的个体并不相同。如果处理效应随与处理选择相关的不可观测因素发生系统性变化，那么这两个群体的平均因果效应可能存在显著差异。我们回顾了这类不可观测异质性对于解释标准工具变量方法及判断其与政策评估的相关性所具有的含义。我们认为，对政策相关参数进行推断，通常需要从受工具变量影响的个体外推至那些会因所考察政策而接受处理的个体。我们讨论了多种可替代标准工具变量方法的方案，这些方案可用于严谨地进行上述外推。我们表明，其中许多方法都可以作为特例纳入一个允许部分识别的一般框架。
Abstract
Instrumental variables (IV) are widely used in economics to address selection on unobservables. Standard IV methods produce estimates of causal effects that are specific to individuals whose behavior can be manipulated by the instrument at hand. In many cases, these individuals are not the same as those who would be induced to treatment by an intervention or policy of interest to the researcher. The average causal effect for the two groups can differ significantly if the effect of the treatment varies systematically with unobserved factors that are correlated with treatment choice. We review the implications of this type of unobserved heterogeneity for the interpretation of standard IV methods and for their relevance to policy evaluation. We argue that making inferences about policy-relevant parameters typically requires extrapolating from the individuals affected by the instrument to the individuals who would be induced to treatment by the policy under consideration. We discuss a variety of alternatives to standard IV methods that can be used to rigorously perform this extrapolation. We show that many of these approaches can be nested as special cases of a general framework that embraces the possibility of partial identification.