Efficiently Deciding Algebraic Equivalence of Bow-Free Acyclic Path Diagrams

Thijs van Ommen*

*Corresponding author for this work

Research output: Contribution to journalConference articleAcademicpeer-review

Abstract

For causal discovery in the presence of latent confounders, constraints beyond conditional independences exist that can enable causal discovery algorithms to distinguish more pairs of graphs. Such constraints are not well-understood yet. In the setting of linear structural equation models without bows, we study algebraic constraints and argue that these provide the most fine-grained resolution achievable. We propose efficient algorithms that decide whether two graphs impose the same algebraic constraints, or whether the constraints imposed by one graph are a subset of those imposed by another graph.

Original languageEnglish
Pages (from-to)3410-3424
Number of pages15
JournalProceedings of Machine Learning Research
Volume244
Publication statusPublished - 2024
Event40th Conference on Uncertainty in Artificial Intelligence, UAI 2024 - Barcelona, Spain
Duration: 15 Jul 202419 Jul 2024

Bibliographical note

Publisher Copyright:
© 2024 Proceedings of Machine Learning Research. All rights reserved.

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