BFCS: Bayes Factors of Covariance Structures

We propose a novel efficient Bayesian method (BFCS) for discovering local causal relationships among triplets of (normally distributed) variables. In our approach, we score covariance structures for each triplet in one go and incorporate available background knowledge in the form of priors to derive posterior probabilities over local causal structures.

Our method is flexible in the sense that it allows for different types of causal structures and assumptions. The proposed algorithm produces stable and conservative posterior probability estimates over local causal structures that can be used to derive an honest ranking of the most meaningful regulatory relationships. The data set contains source code implementing the BFCS algorithm, which is described in the article titled "A Bayesian Approach for Inferring Local Causal Structure in Gene Regulatory Networks" (proceedings.mlr.press/v72/bucur18a.html) by Ioan Gabriel Bucur, Tom van Bussel, Tom Claassen and Tom Heskes, as well as in the follow-up extension titled "Large-scale Local Causal Inference of Gene RegulatoryRelationships" (doi.org/10.1016/j.ijar.2019.08.012).