论文标题
2SL有多种治疗
2SLS with Multiple Treatments
论文作者
论文摘要
我们研究了哪些两阶段最小二乘（2SL）在具有多种处理效果异质性的模型中识别哪些模型。证明两个条件是必要的，足以使2SL识别每种处理的代理特异性效应的正加权总和：平均条件单调性和无交叉效应。我们的识别分析允许任何数量的处理，任何数量的连续或离散工具以及包括协变量的包含。我们提供了可检验的含义和当前的选择行为的特征，即我们的识别条件所隐含的选择。
We study what two-stage least squares (2SLS) identifies in models with multiple treatments under treatment effect heterogeneity. Two conditions are shown to be necessary and sufficient for the 2SLS to identify positively weighted sums of agent-specific effects of each treatment: average conditional monotonicity and no cross effects. Our identification analysis allows for any number of treatments, any number of continuous or discrete instruments, and the inclusion of covariates. We provide testable implications and present characterizations of choice behavior implied by our identification conditions.
