Determinantal Generalizations of Instrumental Variables

Luca Weihs, Bill Robinson, Emilie Dufresne, Jennifer Kenkel, Kaie Kubjas, Reginald L., II McGee, Nhan Nguyen, Elina Robeva, Mathias Drton

Research output: Contribution to journalArticleScientificpeer-review

4 Citations (Scopus)
3 Downloads (Pure)

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 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.
Original languageEnglish
Article number20170009
JournalJournal of causal inference
Volume6
Issue number1
DOIs
Publication statusPublished - Mar 2018
MoE publication typeA1 Journal article-refereed

Keywords

  • trek separation
  • half-trek criterion
  • structural equation models
  • identifiability
  • generic identifiability

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