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来源类型Working Paper
规范类型报告
DOI10.3386/w27546
来源IDWorking Paper 27546
Policy Evaluation with Multiple Instrumental Variables
Magne Mogstad; Alexander Torgovitsky; Christopher R. Walters
发表日期2020-07-20
出版年2020
语种英语
摘要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. Recent research has shown that this condition cannot hold with multiple instruments unless treatment choice is effectively homogeneous. Based on these findings, 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 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 about treatment effects from multiple controlled or natural experiments while still allowing for rich heterogeneity in both treatment effects and choice behavior.
主题Econometrics ; Estimation Methods
URLhttps://www.nber.org/papers/w27546
来源智库National Bureau of Economic Research (United States)
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资源类型智库出版物
条目标识符http://119.78.100.153/handle/2XGU8XDN/585219
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GB/T 7714
Magne Mogstad,Alexander Torgovitsky,Christopher R. Walters. Policy Evaluation with Multiple Instrumental Variables. 2020.
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