A Hybrid Causal Search Algorithm for Latent Variable Models.

Existing score-based causal model search algorithms such as (and a speeded up version, ) are asymptotically correct, fast, and reliable, but make the unrealistic assumption that the true causal graph does not contain any unmeasured confounders. There are several constraint-based causal search algorithms (e.g , or +) that are asymptotically correct without assuming that there are no unmeasured confounders, but often perform poorly on small samples. We describe a combined score and constraint-based algorithm, , that we prove is asymptotically correct. On synthetic data, is only slightly slower than but more accurate than and +.