Regression Discontinuity Designs Using Covariates
研究了在断点回归设计中加入协变量的方法，提出了加性可分离的局部多项式估计量，并给出了新的均方误差展开和稳健偏差校正推断程序，适用于经济学等领域的因果效应估计。
We study regression discontinuity designs when covariates are included in the estimation. We examine local polynomial estimators that include discrete or continuous covariates in an additive separable way, but without imposing any parametric restrictions on the underlying population regression functions. We recommend a covariate-adjustment approach that retains consistency under intuitive conditions and characterize the potential for estimation and inference improvements. We also present new covariate-adjusted mean-squared error expansions and robust bias-corrected inference procedures, with heteroskedasticity-consistent and cluster-robust standard errors. We provide an empirical illustration and an extensive simulation study. All methods are implemented in R and Stata software packages.