Permutation-based Causal Inference Algorithms with Interventions

Learning Bayesian networks using both observational and interventional data is now a fundamentally important problem due to recent technological developments in genomics that generate single-cell gene expression data at a very large scale. In order to utilize this data for learning gene regulatory networks, efficient and reliable causal inference algorithms are needed that can make use of both observational and interventional data. In this paper, we present two algorithms of this type and prove that both are consistent under the faithfulness assumption. These algorithms are interventional adaptations of the Greedy SP algorithm and are the first algorithms using both observational and interventional data with consistency guarantees. Moreover, these algorithms have the advantage that they are non-parametric, which makes them useful for analyzing inherently non-Gaussian gene expression data. In this paper, we present these two algorithms and their consistency guarantees, and we analyze their performance on simulated data, protein signaling data, and single-cell gene expression data.