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.

翻译：有向无环图（DAG）通过其结构编码了特定分布的大量信息。然而，推断这些结构所需的计算量通常随变量数量呈超指数增长，因为推断需要遍历潜在结构的组合爆炸空间。这一困境直到近期才被突破：通过可微度量搜索该空间的方法极大地缩短了搜索时间。尽管被称为NOTEARS的这一技术被广泛视为DAG发现领域的开创性工作，但它为换取可微性而牺牲了一个重要特性——可迁移性。所谓可迁移性，是指从一个数据集发现的结构必须能应用于同一领域的另一个数据集。我们提出D-Struct方法，通过新颖的架构和损失函数在保持完全可微性的同时恢复了所发现结构的可迁移性。由于D-Struct保持可微性，我们的方法可像此前NOTEARS那样轻松集成到现有可微架构中。在实验中，我们从边准确度和结构汉明距离两个维度，在多种场景下对D-Struct进行了实证验证。