  We introduce the local composite quantile regression (LCQR) to causal
inference in regression discontinuity (RD) designs. Kai et al. (2010) 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 (2008). A companion R package rdcqr is developed.
