Robust uniform inference for quantile treatment effects in regression discontinuity designs

Abstract

The practical importance of inference with robustness against large bandwidths for causal effects in regression discontinuity and kink designs is widely recognized. Existing robust methods cover many cases, but do not handle uniform inference for CDF and quantile processes in fuzzy designs. In this light, this paper extends the literature by developing a unified framework of inference with robustness against large bandwidths that applies to uniform inference for quantile treatment effects in fuzzy designs, as well as all the other cases. We present Monte Carlo simulation studies and an empirical application for evaluations of the Oklahoma pre-K program.