Local Composite Quantile Regression for Regression Discontinuity
将局部复合分位数回归用于断点回归设计的因果推断，提出偏差校正和标准误调整的t检验，并开发了R包rdcqr。
We introduce the local composite quantile regression (LCQR) to causal inference in regression discontinuity (RD) designs. Kai, Li and Zou study the efficiency property of LCQR, while we show that its nice boundary performance translates to accurate estimation of treatment effects in RD under a variety of data generating processes. Moreover, we propose a bias-corrected and standard error-adjusted t-test for inference, which leads to confidence intervals with good coverage probabilities. A bandwidth selector is also discussed. For illustration, we conduct a simulation study and revisit a classic example from Lee. A companion R package rdcqr is developed.