  We address the problem of causal effect estimation where hidden confounders
are present, with a focus on two settings: instrumental variable regression
with additional observed confounders, and proxy causal learning. Our approach
uses a singular value decomposition of a conditional expectation operator,
followed by a saddle-point optimization problem, which, in the context of IV
regression, can be thought of as a neural net generalization of the seminal
approach due to Darolles et al. [2011]. Saddle-point formulations have gathered
considerable attention recently, as they can avoid double sampling bias and are
amenable to modern function approximation methods. We provide experimental
validation in various settings, and show that our approach outperforms existing
methods on common benchmarks.
