2024/06/10 by Thijs van Ommen, van Ommen, Thijs
Computer Science · #FOS: Computer and information sciences #FOS: Mathematics #Formal Methods in Verification #Logic, programming, and type systems #Machine Learning (cs.LG) #Machine Learning (stat.ML) #Model-Driven Software Engineering Techniques #Statistics Theory (math.ST)
paper · pdf · doi:10.48550/arxiv.2406.09049
openalex publication_date 2024/06/10 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
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.