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