  Directed acyclic graphs (DAGs) encode a lot of information about a particular
distribution in their structure. However, compute required to infer these
structures is typically super-exponential in the number of variables, as
inference requires a sweep of a combinatorially large space of potential
structures. That is, until recent advances made it possible to search this
space using a differentiable metric, drastically reducing search time. While
this technique -- named NOTEARS -- is widely considered a seminal work in
DAG-discovery, it concedes an important property in favour of
differentiability: transportability. To be transportable, the structures
discovered on one dataset must apply to another dataset from the same domain.
We introduce D-Struct which recovers transportability in the discovered
structures through a novel architecture and loss function while remaining fully
differentiable. Because D-Struct remains differentiable, our method can be
easily adopted in existing differentiable architectures, as was previously done
with NOTEARS. In our experiments, we empirically validate D-Struct with respect
to edge accuracy and structural Hamming distance in a variety of settings.
