Establishing Markov Equivalence in Cyclic Directed Graphs
| 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 |
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| 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
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| Supplementary materials | |
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