检测分组局部平均处理效应并选择真实工具变量
Detecting Grouped Local Average Treatment Effects and Selecting True Instruments
AI总结：
针对内生二元处理的异质性效应及多工具变量场景，提出两步法识别同LATE的依从者群体，可数据驱动选真实工具变量，经模拟与实证（监禁对累犯的影响）验证其一致性与有限样本性质。
摘要：
在内生二元处理具有异质性效应且存在多个工具变量（IV）的情况下，我们提出了一种两步法，用于识别依赖不同工具变量但具有相同局部平均处理效应（LATE）的依从者群体，即使有几个工具变量违反了识别假设。我们利用以下事实：对于满足LATE假设（工具变量有效性和处理对工具变量的单调性）且在各自工具变量给定下产生相同处理倾向的依从者群体的工具变量，其LATE是同质的。我们提出的两步法中，第一步对倾向得分进行聚类，第二步找到具有相同简化形式参数的IV组。在多数假设下（即具有相同处理倾向的工具变量组中，真正满足LATE假设的工具变量是最大的组），我们的方法能以数据驱动的方式识别这些真实工具变量。我们证明该方法是一致的，且能为潜在的LATE提供一致且渐近正态的估计量。我们还提供了模拟研究以探究该方法的有限样本性质，并进行了实证应用，以法官分配作为工具变量，研究监禁对美国累犯率的影响。
英文摘要：
Under an endogenous binary treatment with heterogeneous effects and multiple instruments, we propose a two-step procedure for identifying complier groups with identical local average treatment effects (LATE) despite relying on distinct instruments, even if several instruments violate the identifying assumptions. We use the fact that the LATE is homogeneous for instruments which (i) satisfy the LATE assumptions (instrument validity and treatment monotonicity in the instrument) and (ii) generate identical complier groups in terms of treatment propensities given the respective instruments. We propose a two-step procedure, where we first cluster the propensity scores in the first step and find groups of IVs with the same reduced form parameters in the second step. Under the plurality assumption that within each set of instruments with identical treatment propensities, instruments truly satisfying the LATE assumptions are the largest group, our procedure permits identifying these true instruments in a data driven way. We show that our procedure is consistent and provides consistent and asymptotically normal estimators of underlying LATEs. We also provide a simulation study investigating the finite sample properties of our approach and an empirical application investigating the effect of incarceration on recidivism in the US with judge assignments serving as instruments.
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