Knowledge of the causal structure that underlies a data generating process is essential to answering questions of causal nature. Such questions are abundant in fields that involve decision making such as econometrics, epidemiology, and social sciences. When causal knowledge is unavailable, one can resort to causal discovery algorithms, which attempt to recover causal relationships from data. This talk will present a new algorithm for this task, that combines continuous-constrained optimization with the flexible density estimation capabilities of normalizing flows. In contrast with previous work in this direction, our method combines observational and interventional data to improve identification of the causal graph. We will present empirical results, along with a theoretical justification of our algorithm.