title: A Hybrid Causal Search Algorithm for Latent Variable Models.
abstract: Existing score-based causal model search algorithms such asGES(and a speeded up version,FGS) 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.gRFCI, FCI, orFCI+) 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,GFCI, that we prove is asymptotically correct. On synthetic data,GFCIis only slightly slower thanRFCIbut more accurate thanFCI, RFCIandFCI+.
authors.affiliation: Department of Philosophy, Carngie Mellon University, Pittsburgh, PA.
authors.forename: Juan Miguel
authors.initials: JM
authors.lastname: Ogarrio
authors.affiliation: Department of Philosophy, Carngie Mellon University, Pittsburgh, PA.
authors.forename: Peter
authors.initials: P
authors.lastname: Spirtes
authors.affiliation: Department of Philosophy, Carngie Mellon University, Pittsburgh, PA.
authors.forename: Joe
authors.initials: J
authors.lastname: Ramsey