  Promising results have driven a recent surge of interest in continuous
optimization methods for Bayesian network structure learning from observational
data. However, there are theoretical limitations on the identifiability of
underlying structures obtained from observational data alone. Interventional
data provides much richer information about the underlying data-generating
process. However, the extension and application of methods designed for
observational data to include interventions is not straightforward and remains
an open problem. In this paper we provide a general framework based on
continuous optimization and neural networks to create models for the
combination of observational and interventional data. The proposed method is
even applicable in the challenging and realistic case that the identity of the
intervened upon variable is unknown. We examine the proposed method in the
setting of graph recovery both de novo and from a partially-known edge set. We
establish strong benchmark results on several structure learning tasks,
including structure recovery of both synthetic graphs as well as standard
graphs from the Bayesian Network Repository.
