- Conference Paper
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. Show more
Book titleAdvances in Neural Information Processing Systems 33
Pages / Article No.
Organisational unit09664 - Schölkopf, Bernhard / Schölkopf, Bernhard
NotesDue to the Coronavirus (COVID-19) the conference was conducted virtually.
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