When does IV identification not restrict outcomes?
工具变量识别何时不限制潜在结果？AI 解读 ↓
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工具变量识别何时不限制潜在结果？
- 研究问题
- 当工具变量取有限个值时，哪些选择行为限制能够点识别处理效应，同时不约束潜在结果分布及其与选择行为的关联？
- 方法与思路
- 作者提出必要充分条件，推广 LATE 单调性，并以穷举搜索枚举可识别的选择模型及各模型中的已识别参数。
- 材料陈述的贡献
- 摘要称该条件统一多类 IV 识别结果，发现新选择模型与不可能性结果，并用于交叉随机化处理之间的局部平均互补性。
- 阅读关注点
- 应核对必要充分条件的精确定义、目标效应的适用范围、穷举算法规模，以及经验数据如何检验选择限制的相容性。
- 阅读边界
- 这是 arXiv 预印本，同行评审与期刊接收未核实；摘要未给出条件及证明，不能将有限取值工具的结果直接推广到其他情形。
原文摘要
Many identification results in instrumental variables (IV) models hold without requiring any restrictions on the distribution of potential outcomes, or how those outcomes are correlated with selection behavior. This enables IV models to allow for arbitrary heterogeneity in treatment effects and the possibility of selection on gains in the outcome. I provide a necessary and sufficient condition for treatment effects to be point identified in a manner that does not restrict outcomes, when the instruments take a finite number of values. The condition generalizes the well-known LATE monotonicity assumption, and unifies a wide variety of other known IV identification results. The result also yields a brute-force approach to reveal all selection models that allow for point identification of treatment effects without restricting outcomes, and then enumerate all of the identified parameters within each such selection model. The search uncovers new selection models that yield identification, provides impossibility results for others, and offers opportunities to relax assumptions on selection used in existing literature. An application considers the identification of complementarities between two cross-randomized treatments, obtaining a necessary and sufficient condition on selection for local average complementarities among compliers to be identified in a manner that does not restrict outcomes. I use this result to revisit two empirical settings, one in which the data are incompatible with this restriction on selection, and another in which the data are compatible with the restriction.