  A variety of problems in econometrics and machine learning, including
instrumental variable regression and Bellman residual minimization, can be
formulated as satisfying a set of conditional moment restrictions (CMR). We
derive a general, game-theoretic strategy for satisfying CMR that scales to
nonlinear problems, is amenable to gradient-based optimization, and is able to
account for finite sample uncertainty. We recover the approaches of Dikkala et
al. and Dai et al. as special cases of our general framework before detailing
various extensions and how to efficiently solve the game defined by CMR.
