Dual Instrumental Variable Regression

Krikamol Muandet; Arash Mehrjou; Si Kai Lee; Anant Raj

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arXiv · 1910.12358

Dual Instrumental Variable Regression

Krikamol Muandet·Arash Mehrjou·Si Kai Lee·Anant Raj

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Abstract

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.

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BibTeX RIS

Krikamol Muandet, Arash Mehrjou, Si Kai Lee, Anant Raj. 2019-10-27. Dual Instrumental Variable Regression. https://arxiv.org/abs/1910.12358

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