摘要翻译：
研究了多值处理变量的回归间断设计(RDD)中的辨识和估计问题。我们也允许包含协变量。我们表明，如果没有额外的信息，治疗效果是不确定的。我们给出了导致LATE以及条件LATE的加权平均识别的充要条件。我们证明了如果多重处理条件下的第一阶段不连续性是线性无关的，那么就有可能用方便的可识别权重来识别处理效果的多元加权平均。此外，如果治疗效果不随某些协变量而变化，或者可以假设一个灵活的参数结构，就有可能识别（事实上，过度识别）所有的治疗效果。过度识别可以用来检验这些假设。我们提出了一个简单的估计量，它可以在打包软件中编程为两阶段最小二乘回归，也可以使用打包的标准误差和测试。最后，我们实施了我们的方法，以确定不同类型的保险覆盖对医疗保健利用的影响，如卡德、多布金和梅斯塔斯（2008）。
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英文标题：
《Regression Discontinuity Design with Multivalued Treatments》
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作者：
Carolina Caetano and Gregorio Caetano and Juan Carlos Escanciano
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最新提交年份：
2020
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分类信息：

一级分类：Economics        经济学
二级分类：Econometrics        计量经济学
分类描述：Econometric Theory, Micro-Econometrics, Macro-Econometrics, Empirical Content of Economic Relations discovered via New Methods, Methodological Aspects of the Application of Statistical Inference to Economic Data.
计量经济学理论，微观计量经济学，宏观计量经济学，通过新方法发现的经济关系的实证内容，统计推论应用于经济数据的方法论方面。
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一级分类：Statistics        统计学
二级分类：Methodology        方法论
分类描述：Design, Surveys, Model Selection, Multiple Testing, Multivariate Methods, Signal and Image Processing, Time Series, Smoothing, Spatial Statistics, Survival Analysis, Nonparametric and Semiparametric Methods
设计，调查，模型选择，多重检验，多元方法，信号和图像处理，时间序列，平滑，空间统计，生存分析，非参数和半参数方法
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英文摘要：
  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).
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PDF链接：
https://arxiv.org/pdf/2007.00185