Learning causal Bayesian networks from incomplete observational data and interventions

This paper proposes a new method for learning causal Bayesian networks from incomplete observational data and interventions. We extend our Greedy Equivalence Search-Expectation Maximization (GES-EM) algorithm [2], initially proposed to learn Bayesian networks from incomplete observational data, by adding a new step allowing the discovery of correct causal relationships using interventional data. Two intervention selection approaches are proposed: an adaptive one, where interventions are done sequentially and where the impact of each intervention is considered before starting the next one, and a non-adaptive one, where the interventions are executed simultaneously. An experimental study shows the merits of the new version of the GES-EM algorithm by comparing the two selection approaches.