  This paper develops estimation and inference methods for censored quantile
regression models with high-dimensional controls. The methods are based on the
application of double/debiased machine learning (DML) framework to the censored
quantile regression estimator of Buchinsky and Hahn (1998). I provide valid
inference for low-dimensional parameters of interest in the presence of
high-dimensional nuisance parameters when implementing machine learning
estimators. The proposed estimator is shown to be consistent and asymptotically
normal. The performance of the estimator with high-dimensional controls is
illustrated with numerical simulation and an empirical application that
examines the effect of 401(k) eligibility on savings.
