We propose a regularized projection score method for estimating treatment effects in quantile regression in the presence of high-dimensional confounding covariates. We show that the proposed estimator of the treatment effects is consistent and asymptotically normal, with a root-n rate of convergence. We also provide an efficient algorithm for the proposed estimator. This algorithm can be easily implemented using existing software. Furthermore, we propose and validate a refitted wild

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... g approach for variance estimation. This enables us to construct confidence intervals for treatment effects in high-dimensional settings. Simulation studies are carried out to evaluate the finite sample performance of the proposed estimator. A GDP growth rate dataset is used to demonstrate the applications of the method.