Originality: This work builds on recent work on adapting deep networks for use with instrumental variables (DeepIV [Hartford et al 2017] & Adversarial GMM (AGMM) [Lewis & Syrgkanis 2018]) but adapts the optimally weighted GMM [Hansen 1982] (OWGMM) for the task. AGMM is probably most similar in that it is also an adversarial loss, but the variational reformulation presented in this paper results in a far simpler algorithm. Quality: I thought this was great paper. The variational reformulation of OWGMM leads to a far simpler objective function that neatly leverages the explosion of recent work in adversarial learning (GANs, etc.) by replacing a large number of moment conditions with a single adversarial network. That said, given that the method appears useful in practice, I would have liked to see more detailed experiments on the practical considerations.