Learning causal relationships solely from observational data provides insufficient information about the underlying causal mechanism and the search space of possible causal graphs. As a result, often the search space can grow exponentially for approaches such as Greedy Equivalence Search (GES) that uses a score-based approach to search the space of equivalence classes of graphs. Prior causal information such as the presence or absence of a causal edge can be leveraged to guide the discovery process towards a more restricted and accurate search space. In this study, we present KGS, a knowledge-guided greedy score-based causal discovery approach that uses observational data and structural priors (causal edges) as constraints to learn the causal graph. KGS is a novel application of knowledge constraints that can leverage any of the following prior edge information between any two variables: the presence of a directed edge, the absence of an edge, and the presence of an undirected edge. We extensively evaluate KGS across multiple settings in both synthetic and benchmark real-world datasets. Our experimental results demonstrate that structural priors of any type and amount are helpful and guide the search process towards an improved performance and early convergence.

翻译：仅从观测数据中学习因果关系，对于底层因果机制以及可能因果图的搜索空间所提供的信息不足。因此，对于诸如贪婪等价搜索（GES）这类使用基于评分的方法来搜索图等价类空间的方法而言，搜索空间常常会呈指数级增长。先验因果信息（例如存在或不存在因果边）可用于引导发现过程，使其走向更受限制且更精确的搜索空间。在本研究中，我们提出了KGS，一种知识引导的基于评分的贪婪因果发现方法，它利用观测数据和结构先验（因果边）作为约束来学习因果图。KGS是一种新颖的知识约束应用，能够利用任意两个变量之间的以下任何先验边信息：存在有向边、不存在边以及存在无向边。我们在合成数据集和基准真实世界数据集的多种设置下对KGS进行了广泛评估。我们的实验结果表明，任何类型和数量的结构先验都是有益的，并能够引导搜索过程实现更优的性能和更早收敛。