Dual Instrumental Variable Regression
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.
Cite
@article{arxiv.1910.12358,
title = {Dual Instrumental Variable Regression},
author = {Krikamol Muandet and Arash Mehrjou and Si Kai Lee and Anant Raj},
journal= {arXiv preprint arXiv:1910.12358},
year = {2020}
}
Comments
Advances in Neural Information Processing Systems 33 (NeurIPS 2020)