Bias‐Aware Inference in Fuzzy Regression Discontinuity Designs
针对模糊断点回归设计，提出基于局部线性回归的偏差感知置信集，避免传统Delta方法近似问题，适用于离散运行变量、甜甜圈设计及弱识别等场景。
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, they are also valid under a wide range of other empirically relevant conditions, such as setups with discrete running variables, donut designs, and weak identification.