Establishing Markov Equivalence in Cyclic Directed Graphs

Open Access
Authors
Publication date 2023
Journal Proceedings of Machine Learning Research
Event 39th Conference on Uncertainty in Artificial Intelligence, UAI 2023
Volume | Issue number 216
Pages (from-to) 433-442
Number of pages 10
Organisations
  • Faculty of Science (FNWI) - Korteweg-de Vries Institute for Mathematics (KdVI)
Abstract

We present a new, efficient procedure to establish Markov equivalence between directed graphs that may or may not contain cycles under the dseparation criterion. It is based on the Cyclic Equivalence Theorem (CET) in the seminal works on cyclic models by Thomas Richardson in the mid'90s, but now rephrased from an ancestral perspective. The resulting characterization leads to a procedure for establishing Markov equivalence between graphs that no longer requires explicit tests for d-separation, leading to a significantly reduced algorithmic complexity. The conceptually simplified characterization may help to reinvigorate theoretical research towards sound and complete cyclic discovery in the presence of latent confounders.

Document type Article
Note Proceedings of the Thirty-Ninth Conference on Uncertainty in Artificial Intelligence, 31-4 August 2023, Pittsburgh, PA, USA. - With supplementary material. - Corrected version published on ArXiv.
Language English
Published at https://doi.org/10.48550/arXiv.2309.03092
Published at https://proceedings.mlr.press/v216/claassen23a.html
Other links https://www.scopus.com/pages/publications/85170105694
Downloads
claassen23a (Final published version)
2309.03092 (Other version)
Supplementary materials
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