In this paper, we propose deep partial least squares for the estimation of high-dimensional nonlinear instrumental variable regression. As a precursor to flexible neural network architecture, our methodology uses dimension reduction and feature selection from set instruments covariates. A central theoretical result, due Brillinger (2012) Selected Works Daving Brillinger. 589-606, shows that pro...