  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.
