NeurIPS 2020poster113 citations
Dual Instrumental Variable Regression
Krikamol Muandet, Arash Mehrjou, Si Kai Lee, Anant Raj
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.
BibTeX
@inproceedings{NEURIPS2020_1c383cd3,
author = {Muandet, Krikamol and Mehrjou, Arash and Lee, Si Kai and Raj, Anant},
booktitle = {Advances in Neural Information Processing Systems},
editor = {H. Larochelle and M. Ranzato and R. Hadsell and M.F. Balcan and H. Lin},
pages = {2710--2721},
publisher = {Curran Associates, Inc.},
title = {Dual Instrumental Variable Regression},
url = {https://proceedings.neurips.cc/paper_files/paper/2020/file/1c383cd30b7c298ab50293adfecb7b18-Paper.pdf},
volume = {33},
year = {2020}
}