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Dual Instrumental Variable Regression
Krikamol Muandet · Arash Mehrjou · Si Kai Lee · Anant Raj

Wed Dec 09 09:00 AM -- 11:00 AM (PST) @ Poster Session 3 #782

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.

Author Information

Krikamol Muandet (Max Planck Institute for Intelligent Systems)
Arash Mehrjou (Max Planck Institute)
Si Kai Lee (Chicago Booth School of Business)
Anant Raj (Max Planck Institute for Intelligent Systems)

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