Decision making based on statistical association alone can be a dangerous endeavor due to non-causal associations. Ideally, one would rely on causal relationships that enable reasoning about the effect of interventions. Several methods have been proposed to discover such relationships from observational and interventional data. Among them, GraN-DAG, a method that relies on the constrained optimization of neural networks, was shown to produce state-of-the-art results among algorithms relying purely on observational data. However, it is limited to observational data and cannot make use of interventions. In this work, we extend GraN-DAG to support interventional data and show that this improves its ability to infer causal structures.