A Hybrid Causal Search Algorithm for Latent Variable Models

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Juan Miguel Ogarrio, Peter Spirtes, Joe Ramsey ;
Proceedings of the Eighth International Conference on Probabilistic Graphical Models, PMLR 52:368-379, 2016.

Abstract

Existing score-based causal model search algorithms such as \textitGES (and a speeded up version, \textitFGS) 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 \textitRFCI, \emphFCI, or \emphFCI+) 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, \emphGFCI, that we prove is asymptotically correct. On synthetic data, \textitGFCI is only slightly slower than \emphRFCI but more accurate than \textitFCI, \textitRFCI and \textitFCI+.

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