Krikamol Muandet, Arash Mehrjou, Si Kai Lee, Anant Raj
arXiv 27 Oct 2019 · Statistics — Machine Learning · 17 citations (OpenAlex)
arXiv:1910.12358 · PDF · DOI · OpenAlex · Extracted main text
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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The works this paper leans on most, across its whole bibliography — not restricted to papers in our corpus. Ranked by composite intensity, which combines how often a work is mentioned, how many sections mention it, and how much of that falls in the main text rather than the appendix.
| Reference | Intensity | Mentions | Sections | Main text | |
|---|---|---|---|---|---|
| 1 | Rahul Singh, Maneesh Sahani, and Arthur Gretton (2019) Kernel instrumental variable regression | 1.000 | 15 | 5 | 100% |
| 2 | Bo Dai, Niao He, Yunpeng Pan, Byron Boots, and Le Song (2017) Learning from Conditional Distributions via Dual Embeddings | 1.000 | 12 | 4 | 100% |
| 3 | Jason Hartford, Greg Lewis, Kevin Leyton-Brown, and Matt Taddy (2017) Deep IV: A flexible approach for counterfactual prediction | 1.000 | 12 | 4 | 100% |
| 4 | Andrew Bennett, Nathan Kallus, and Tobias Schnabel (2019) Deep generalized method of moments for instrumental variable analysis | 0.928 | 4 | 3 | 100% |
| 5 | Joshua D. Angrist and Jörn-Steffen Pischke (2008) Mostly Harmless Econometrics: An Empiricist's Companion | 0.874 | 10 | 2 | 100% |
| 6 | Whitney K. Newey and James L. Powell (2003) Instrumental variable estimation of nonparametric models | 0.874 | 7 | 2 | 100% |
| 7 | Alexander Shapiro, Darinka Dentcheva, and Andrzej Ruszczynski (2014) Lectures on Stochastic Programming: Modeling and Theory, Second Edition | 0.843 | 4 | 3 | 75% |
| 8 | Krikamol Muandet, Kenji Fukumizu, Bharath Sriperumbudur, and Bernhar… (2017) Kernel mean embedding of distributions: A review and beyond self | 0.737 | 4 | 4 | 50% |
| 9 | Luofeng Liao, You-Lin Chen, Zhuoran Yang, Bo Dai, Zhaoran Wang, and… (2020) Provably efficient neural estimation of structural equation model: An adversarial approach | 0.737 | 3 | 3 | 67% |
| 10 | Joshua D. Angrist, Guido W. Imbens, and Donald B. Rubin (1996) Identification of causal effects using instrumental variables | 0.737 | 3 | 2 | 100% |
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