Regression discontinuity designs have become one of the most popular research designs in empirical economics. We argue, however, that widely used approaches to building confidence intervals in regression discontinuity designs exhibit suboptimal behavior in practice: In a simulation study calibrated to high-profile applications of regression discontinuity designs, existing methods either have systematic under-coverage or have wider-than-necessary intervals. We propose a new approach, partially linear regression discontinuity inference (PLRD), and find it to address shortcomings of existing methods: Throughout our experiments, confidence intervals built using PLRD are both valid and short. We also provide large-sample guarantees for PLRD under smoothness assumptions.

翻译：回归断点设计已成为实证经济学中最受欢迎的研究设计之一。然而，我们认为，在回归断点设计中广泛使用的置信区间构建方法在实践中表现出次优行为：在一项基于回归断点设计的高影响力应用校准的模拟研究中，现有方法要么存在系统性覆盖不足的问题，要么其区间宽度超出必要范围。我们提出了一种新方法——部分线性回归断点推断（PLRD），并发现它能够解决现有方法的缺陷：在所有实验中，使用PLRD构建的置信区间既有效又紧凑。我们还在平滑性假设下为PLRD提供了大样本保证。