Including Covariates in the Regression Discontinuity Design
提出一种完全非参数的核方法，在断点回归设计中纳入观测到的协变量，以提高处理效应估计的精度，并证明协变量能降低渐近方差、减少偏误。
This paper proposes a fully nonparametric kernel method to account for observed covariates in regression discontinuity designs (RDD), which may increase precision of treatment effect estimation. It is shown that conditioning on covariates reduces the asymptotic variance and allows estimating the treatment effect at the rate of one-dimensional nonparametric regression, irrespective of the dimension of the continuously distributed elements in the conditioning set. Furthermore, the proposed method may decrease bias and restore identification by controlling for discontinuities in the covariate distribution at the discontinuity threshold, provided that all relevant discontinuously distributed variables are controlled for. To illustrate the estimation approach and its properties, we provide a simulation study and an empirical application to an Austrian labor market reform.