论文标题
监督整个DAG因果发现
Supervised Whole DAG Causal Discovery
论文作者
论文摘要
我们建议以监督的方式从数据中学习因果结构的任务。通过监督学习的学习因果方向的现有工作仅限于学习成对关系，并且不适合整个DAG发现。我们提出了一种新颖的方法，将整个DAG结构发现建模为监督学习。为了解决该问题，我们建议使用与问题域保持良好对齐的置换模型。我们对尺寸10,20,50,100和真实数据的合成图广泛评估了所提出的方法，并与以前的多种方法相比显示出令人鼓舞的结果。
We propose to address the task of causal structure learning from data in a supervised manner. Existing work on learning causal directions by supervised learning is restricted to learning pairwise relation, and not well suited for whole DAG discovery. We propose a novel approach of modeling the whole DAG structure discovery as a supervised learning. To fit the problem in hand, we propose to use permutation equivariant models that align well with the problem domain. We evaluate the proposed approach extensively on synthetic graphs of size 10,20,50,100 and real data, and show promising results compared with a variety of previous approaches.
