Policy evaluation with multiple instrumental variables
Policy evaluation with multiple instrumental variables
复制标题
多工具变量的政策评估
DOI：
10.1016/j.jeconom.2024.105718
复制
发表时间：
2024
影响因子：
6.3
通讯作者：
Walters, Christopher R.
中科院分区：
文献类型：
--
作者：
Mogstad, Magne;Torgovitsky, Alexander;Walters, Christopher R.
边际处理效应方法被广泛用于带有工具变量的因果推断和政策评价。然而，它们从根本上依赖于治疗选择行为的众所周知的单调性(跨越门槛)条件。这种情况不能适用于多种器械，除非治疗选择是有效的同质的。在较弱的部分单调条件下，我们发展了一个新的边际处理效应框架。标准选择理论隐含着部分单调性条件，即使在多种工具存在的情况下，也允许存在丰富的未被观察到的异质性。新的框架可以被视为对同一观察到的治疗变量有多个不同的选择模型，所有这些模型都必须与数据和彼此一致。使用这个框架，我们开发了一种方法，用于部分识别明确陈述的、与政策相关的目标参数，同时允许广泛的非参数形状限制和参数函数形式假设。我们展示了如何使用该方法将多个工具组合在一起，以产生比单独使用每个工具所获得的更具信息性的经验结论。该方法为从多个受控或自然实验中提取和汇总信息提供了蓝图，同时仍允许在治疗效果和选择行为方面存在丰富的未观察到的异质性。
Marginal treatment effect methods are widely used for causal inference and policy evaluation with instrumental variables. However, they fundamentally rely on the well-known monotonicity (threshold-crossing) condition on treatment choice behavior. This condition cannot hold with multiple instruments unless treatment choice is effectively homogeneous. We develop a new marginal treatment effect framework under a weaker, partial monotonicity condition. The partial monotonicity condition is implied by standard choice theory and allows for rich unobserved heterogeneity even in the presence of multiple instruments. The new framework can be viewed as having multiple different choice models for the same observed treatment variable, all of which must be consistent with the data and with each other. Using this framework, we develop a methodology for partial identification of clearly stated, policy-relevant target parameters while allowing for a wide variety of nonparametric shape restrictions and parametric functional form assumptions. We show how the methodology can be used to combine multiple instruments together to yield more informative empirical conclusions than one would obtain by using each instrument separately. The methodology provides a blueprint for extracting and aggregating information from multiple controlled or natural experiments while still allowing for rich unobserved heterogeneity in both treatment effects and choice behavior.