  We present a novel algorithm for non-linear instrumental variable (IV)
regression, DualIV, which simplifies traditional two-stage methods via a dual
formulation. Inspired by problems in stochastic programming, we show that
two-stage procedures for non-linear IV regression can be reformulated as a
convex-concave saddle-point problem. Our formulation enables us to circumvent
the first-stage regression which is a potential bottleneck in real-world
applications. We develop a simple kernel-based algorithm with an analytic
solution based on this formulation. Empirical results show that we are
competitive to existing, more complicated algorithms for non-linear
instrumental variable regression.
