Score-based vs Constraint-based Causal Learning in the Presence of Confounders

We compare score-based and constraint-based learning in the presence of latent confounders. We use a greedy search strategy to identify the best fitting maximal ancestral graph (MAG) from continuous data, under the assumption of multivariate normality. Scoring maximal ancestral graphs is based on (a) residual iterative conditional fitting [Drton et al., 2009] for obtaining maximum likelihood estimates for the parameters of a given MAG and (b) factorization and score decomposition results for mixed causal graphs [Richardson, 2009, Nowzohour et al., 2015]. We compare the score-based approach in simulated settings with two standard constraintbased algorithms: FCI and conservative FCI. Results show a promising performance of the greedy search algorithm.