Inference in regression discontinuity designs under local randomization
Inference in regression discontinuity designs under local randomization
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DOI：
10.1177/1536867x1601600205
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发表时间：
2016-01-01
期刊：
影响因子：
4.8
通讯作者：
Vazquez-Bare, Gonzalo
中科院分区：
文献类型：
--
作者：
Cattaneo, Matias D.;Titiunik, Rocio;Vazquez-Bare, Gonzalo
我们介绍了rdlocrand包，它包含四个命令，用于在局部随机化假设下进行回归不连续性（RD）设计中的有限样本推断，遵循Cattaneo，Frandsen和Titiunik（2015，因果推断杂志3：1-24）和Cattaneo，Titiunik和Vazquez-Bare（2016，密歇根大学工作论文，http://www-personal.umich.edu/similar to titiunik/papers/CattaneoTitiunikVazquezBare2015_wp.pdf）中提出的框架和方法。假设一个已知的分配机制，为单位接近RD截止，这些功能实现了各种程序的基础上随机化推理技术。首先，rdrandinf命令使用随机化方法在不同假设下进行点估计、假设检验和置信区间估计。第二，rdwinselect命令使用有限样本方法来选择一个接近截止值的窗口，在该窗口中随机治疗分配的假设最合理。第三，rdsensitivity命令使用随机化技术对RD截止点周围的不同窗口进行一系列假设检验，可用于评估方法的灵敏度并通过反演构建置信区间。最后，rdrbounds命令实现Rosenbaum（2002，Observational Studies [Springer]）局部随机化条件下RD设计的敏感性界限。还提供了具有相同语法和功能的配套R函数。
We introduce the rdlocrand package, which contains four commands to conduct finite-sample inference in regression discontinuity (RD) designs under a local randomization assumption, following the framework and methods proposed in Cattaneo, Frandsen, and Titiunik (2015, Journal of Causal Inference 3: 1-24) and Cattaneo, Titiunik, and Vazquez-Bare (2016, Working Paper, University of Michigan, http://www-personal.umich.edu/similar to titiunik/papers/ CattaneoTitiunikVazquezBare2015_wp.pdf). Assuming a known assignment mechanism for units close to the RD cutoff, these functions implement a variety of procedures based on randomization inference techniques. First, the rdrandinf command uses randomization methods to conduct point estimation, hypothesis testing, and confidence interval estimation under different assumptions. Second, the rdwinselect command uses finite-sample methods to select a window near the cutoff where the assumption of randomized treatment assignment is most plausible. Third, the rdsensitivity command uses randomization techniques to conduct a sequence of hypothesis tests for different windows around the RD cutoff, which can be used to assess the sensitivity of the methods and to construct confidence intervals by inversion. Finally, the rdrbounds command implements Rosenbaum (2002, Observational Studies [Springer]) sensitivity bounds for the context of RD designs under local randomization. Companion R functions with the same syntax and capabilities are also provided.