发表机构
Renmin University of China; Vanderbilt University; The University of Osaka（中国人民大学; 范德堡大学; 大阪大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本文提出熵平衡重加权方法，在断点回归与回归扭点设计中实现协变量调整，对非线性估计量保持一致性与效率提升，并带来额外效率增益。
AI 中文摘要
在断点回归设计（RDDs）和回归扭点设计（RKDs）中纳入协变量是标准做法，但这样做的理论依据通常不适用于线性估计量之外的场景。本文提出了一种新颖的熵平衡重加权方法，用于在RDDs和RKDs的一般框架内进行协变量调整。虽然传统的基于回归的协变量调整方法通常无法对非线性估计量（如分位数处理效应）提供一致估计，我们的重加权方法在实现一致性的同时提高了效率。此外，即使在基于回归的协变量调整方法已经能够提高效率的情况下，我们的方法也能带来额外的效率增益。模拟研究证实了这些理论发现。我们展示了一个实证应用，在该应用中，我们的协变量调整产生了统计上显著的结果，而如果不进行协变量调整则无法获得这些结果。
英文摘要
It is standard practice to include covariates in regression discontinuity designs (RDDs) and regression kink designs (RKDs), but the theoretical justification for doing so does not generally extend beyond linear estimands. This paper proposes a novel entropy balancing reweighting approach for covariate adjustment within a general framework of RDDs and RKDs. While conventional regression-based covariate adjustment methods generally fail to deliver consistent estimation for nonlinear estimands such as quantile treatment effects, our reweighting approach achieves consistency while improving efficiency. Moreover, even in settings where the regression-based covariate adjustment method already improves efficiency, our approach can deliver additional efficiency gains. Simulation studies corroborate these theoretical findings. We present an empirical application in which our covariate adjustment yields statistically significant results that would not be obtained without covariate adjustment.
