Unconditional quantile regression with high‐dimensional data

This paper considers estimation and inference for heterogeneous counterfactual effects with high‐dimensional data. We propose a novel robust score debiased of the unconditional quantile regression (Firpo, Fortin, Lemieux (2009)) as measure marginal effects. multiplier bootstrap develop asymptotic theories to guarantee size control in large sample. Simulation studies support our theories. Applying proposed method Job Corps survey data, we find that policy, which counterfactually extends duration exposures training program, will be effective especially targeted subpopulations lower potential wage earners.