We propose new confidence sets (CSs) for the regression discontinuity parameter in fuzzy designs. Our CSs are based on local linear regression, and are bias-aware, in the sense that they take possible bias explicitly into account. Their construction shares similarities with that of Anderson-Rubin CSs in exactly identified instrumental variable models, and thereby avoids issues with "delta method" approximations that underlie most commonly used existing inference methods for fuzzy regression discontinuity analysis. Our CSs are asymptotically equivalent to existing procedures in canonical settings with strong identification and a continuous running variable. However, due to their particular construction they are also valid under a wide range of empirically relevant conditions in which existing methods can fail, such as setups with discrete running variables, donut designs, and weak identification.

翻译：我们提出了模糊设计中回归不连续参数的新置信集。该置信集基于局部线性回归，具有偏差感知特性，即明确考虑潜在偏差。其构建方式与恰好识别工具变量模型中的安德森-鲁宾置信集类似，从而避免了当前模糊回归不连续分析中最常用推断方法所依赖的"德尔塔法"近似问题。在具有强识别和连续运行变量的标准情境下，我们的置信集与现有方法渐近等价。但由于其独特的构造方式，在离散运行变量、甜甜圈设计及弱识别等现有方法可能失效的多种实证相关条件下，该置信集依然有效。