Greedy equivalence search in the presence of latent confounders

We investigate Greedy PAG Search (GPS) for score-based causal discovery over equivalence classes, similar to the famous Greedy Equivalence Search algorithm, except now in the presence of latent confounders. It is based on a novel characterization of Markov equivalence classes for MAGs, that not only improves state-of-the-art identification of Markov equivalence between MAGs to linear time complexity for sparse graphs, but also allows for efficient traversal over equivalence classes in the space of all MAGs. The resulting GPS algorithm is evaluated against several existing alternatives and found to show promising performance, both in terms of speed and accuracy.