  We explore if it is possible to learn a directed acyclic graph (DAG) from
data without imposing explicitly the acyclicity constraint. In particular, for
Gaussian distributions, we frame structural learning as a sparse matrix
factorization problem and we empirically show that solving an
$\ell_1$-penalized optimization yields to good recovery of the true graph and,
in general, to almost-DAG graphs. Moreover, this approach is computationally
efficient and is not affected by the explosion of combinatorial complexity as
in classical structural learning algorithms.
