Using a General Prior Knowledge Graph to Improve Data-Driven Causal Network Learning.

We describe a method “Kg2Causal” for using a large-scale, general-purpose biomedical knowledge graph as a prior for data-driven causal network structure learning. Given a set of observed nodes in a dataset, and some relationship edges between the nodes derived from a knowledge graph, Kg2Causal uses the knowledge graph-derived edges to guide the data-driven inference of a causal Bayesian network. We tested Kg2Causal on several real-world biological datasets with known ground-truth networks and demonstrate improvement in network learning accuracy, relative to a baseline of an uninformative network structure prior. We also demonstrate the application of our method if data are collected under different experimental conditions including interventions on the observed variables.