The confidence intervals (CIs) commonly reported in empirical fuzzy regression discontinuity studies are justified by theoretical arguments which assume that the running variable is continuously distributed with positive density around the cutoff, and that the jump in treatment probabilities at the cutoff is "large". In this paper, we provide new confidence sets (CSs) that do not rely on such assumptions. Their construction is analogous to that of Anderson-Rubin CSs in the literature on instrumental variable models. Our CSs are based on local linear regression, and are bias-aware, in the sense that they explicitly take the possible smoothing bias into account. They are valid under a wide range of empirically relevant conditions in which existing CIs generally fail. These conditions include discrete running variables, donut designs, and weak identification. But our CS also perform favorably relative to existing CIs in the canonical setting with a continuous running variable, and can thus be used in all fuzzy regression discontinuity applications.

翻译：实验性模糊回归不连续性研究中通常报告的置信间隔(CIs)有理论依据,这些理论依据假设运行变量在截断点周围持续分布,且正密度为正值,而截断点处理概率的跳跃是“大 ” 。 在本文中,我们提供了不依赖这些假设的新的置信套( CSs) 。 它们与工具变量模型文献中的Anderson-Rubin CS的构造类似。 我们的 CS基于本地的线性回归,是偏向性,即它们明确将可能的平滑偏差考虑在内。 它们在现有CIRC通常失败的广泛的经验相关条件下是有效的。 这些条件包括离散运行变量、 甜甜甜点设计以及薄弱的识别。 但是我们的 CS 也以连续运行变量与卡尼卡尼基环境下的现有 CIS 相比表现得更好, 因而可以用于所有模糊回归不连续运行的不连续应用。