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
Text
Muandet et al. "Dual Instrumental Variable Regression." Neural Information Processing Systems, 2020.Markdown
[Muandet et al. "Dual Instrumental Variable Regression." Neural Information Processing Systems, 2020.](https://mlanthology.org/neurips/2020/muandet2020neurips-dual/)BibTeX
@inproceedings{muandet2020neurips-dual,
title = {{Dual Instrumental Variable Regression}},
author = {Muandet, Krikamol and Mehrjou, Arash and Lee, Si Kai and Raj, Anant},
booktitle = {Neural Information Processing Systems},
year = {2020},
url = {https://mlanthology.org/neurips/2020/muandet2020neurips-dual/}
}