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 language | English |
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Pages (from-to) | 3410-3424 |
Number of pages | 15 |
Journal | Proceedings of Machine Learning Research |
Volume | 244 |
Publication status | Published - 2024 |
Event | 40th Conference on Uncertainty in Artificial Intelligence, UAI 2024 - Barcelona, Spain Duration: 15 Jul 2024 → 19 Jul 2024 |
Bibliographical note
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