Gene regulatory networks play a crucial role in controlling an organism’s biological processes, which is why there is significant interest in developing computational methods that are able to extract their structure from high-throughput genetic data. 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" (http://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" (https://doi.org/10.1016/j.ijar.2019.08.012). The data set also contains simulated data necessary for reproducing the figures in the article as well as routines necessary for recreating it. This research is presented in Chapter 3 of the PhD thesis titled "Being Bayesian about Causal Inference" by Ioan Gabriel Bucur. The code is written in the R and C++ programming languages.