论文标题
带有多价治疗的回归不连续设计
Regression Discontinuity Design with Multivalued Treatments
论文作者
论文摘要
我们研究了具有多价处理变量的回归不连续设计（RDD）中的识别和估计。我们还允许包括协变量。我们表明，如果没有其他信息，则无法确定治疗效果。我们提供了必要和充分的条件，从而导致鉴定有条件的LATE的LATE和加权平均值。我们表明，如果以协变量为条件的多种处理的第一阶段不连续性是线性独立的，那么可以识别治疗效果的多元加权平均值，并具有方便的可识别权重。此外，如果可以假定治疗效果随某些协变量而变化，或者可以假定柔性参数结构，则可以识别（实际上，过度识别）所有治疗效果。过度识别可用于测试这些假设。我们提出了一个简单的估计器，可以在包装软件中作为两阶段最小二乘回归进行编程，也可以使用包装的标准错误和测试。最后，我们实施了我们的方法来确定不同类型的保险范围对医疗保健利用的影响，如卡，Dobkin和Maestas（2008）。
We study identification and estimation in the Regression Discontinuity Design (RDD) with a multivalued treatment variable. We also allow for the inclusion of covariates. We show that without additional information, treatment effects are not identified. We give necessary and sufficient conditions that lead to identification of LATEs as well as of weighted averages of the conditional LATEs. We show that if the first stage discontinuities of the multiple treatments conditional on covariates are linearly independent, then it is possible to identify multivariate weighted averages of the treatment effects with convenient identifiable weights. If, moreover, treatment effects do not vary with some covariates or a flexible parametric structure can be assumed, it is possible to identify (in fact, over-identify) all the treatment effects. The over-identification can be used to test these assumptions. We propose a simple estimator, which can be programmed in packaged software as a Two-Stage Least Squares regression, and packaged standard errors and tests can also be used. Finally, we implement our approach to identify the effects of different types of insurance coverage on health care utilization, as in Card, Dobkin and Maestas (2008).
