Dual Instrumental Variable Regression

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Tác giả: Si Kai Lee, Arash Mehrjou, Krikamol Muandet, Anant Raj

Ngôn ngữ: eng

Ký hiệu phân loại: 517.23 [Unassigned]

Thông tin xuất bản: 2019

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Bộ sưu tập: Metadata

ID: 163536

Comment: Advances in Neural Information Processing Systems 33 (NeurIPS 2020)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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