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Journal of Causal Inference

Ed. by Imai, Kosuke / Pearl, Judea / Petersen, Maya Liv / Sekhon, Jasjeet / van der Laan, Mark J.

2 Issues per year

Online
ISSN
2193-3685
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Determinantal Generalizations of Instrumental Variables

Luca Weihs / Bill Robinson / Emilie Dufresne / Jennifer Kenkel / Kaie Kubjas Reginald McGee II / McGee II Reginald / Nhan Nguyen / Elina Robeva / Mathias Drton
Published Online: 2017-12-08 | DOI: https://doi.org/10.1515/jci-2017-0009

Abstract

Linear structural equation models relate the components of a random vector using linear interdependencies and Gaussian noise. Each such model can be naturally associated with a mixed graph whose vertices correspond to the components of the random vector. The graph contains directed edges that represent the linear relationships between components, and bidirected edges that encode unobserved confounding. We study the problem of generic identifiability, that is, whether a generic choice of linear and confounding effects can be uniquely recovered from the joint covariance matrix of the observed random vector. An existing combinatorial criterion for establishing generic identifiability is the half-trek criterion (HTC), which uses the existence of trek systems in the mixed graph to iteratively discover generically invertible linear equation systems in polynomial time. By focusing on edges one at a time, we establish new sufficient and new necessary conditions for generic identifiability of edge effects extending those of the HTC. In particular, we show how edge coefficients can be recovered as quotients of subdeterminants of the covariance matrix, which constitutes a determinantal generalization of formulas obtained when using instrumental variables for identification. While our results do not completely close the gap between existing sufficient and necessary conditions we find, empirically, that our results allow us to prove the generic identifiability of many more mixed graphs than the prior state-of-the-art.

Keywords: trek separation; half-trek criterion; structural equation models; identifiability,generic identifiability

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About the article

Published Online: 2017-12-08

Published in Print: 2018-03-26


Citation Information: Journal of Causal Inference, Volume 6, Issue 1, 20170009, ISSN (Online) 2193-3685, DOI: https://doi.org/10.1515/jci-2017-0009.

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