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Understanding treatment effect heterogeneity in observational studies is of great practical importance since the same treatment may affect different individuals differently. Quantile regression provides a natural framework for modeling such heterogeneity. In this talk, I propose a new estimator for quantile treatment effects in the presence of high-dimensional covariates. The estimator combines a de-biased L1-penalized regression adjustment with a novel quantile-specific covariate balancing scheme. Conceptually, this approach differs from classical de-biasing approaches as the proposed bias correction directly targets the conditional quantile function and not the high-dimensional regression vector. I present a comprehensive study of the theoretical properties of the estimator, including weak convergence of the quantile treatment effect process. The key finding is that the estimator is semi-parametric efficient. I illustrate its finite-sample performance through Monte Carlo experiments and an empirical example, dealing with the differential effect of mothers’ education on infant birth weight.