  In this study, we investigate estimation and inference on a low-dimensional
causal parameter in the presence of high-dimensional controls in an
instrumental variable quantile regression. Our proposed econometric procedure
builds on the Neyman-type orthogonal moment conditions of a previous study
Chernozhukov, Hansen and Wuthrich (2018) and is thus relatively insensitive to
the estimation of the nuisance parameters. The Monte Carlo experiments show
that the estimator copes well with high-dimensional controls. We also apply the
procedure to empirically reinvestigate the quantile treatment effect of 401(k)
participation on accumulated wealth.
