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.

本研究提出了一种利用知识约束来指导因果关系探索的新方法，该方法称为KGS，通过使用观察数据和结构先验（因果边缘）对因果图进行约束学习，并在合成和基准真实世界数据集中广泛评估KGS。实验结果表明，任何类型和数量的结构先验都有助于改善探索过程的性能和早期收敛。

KGS: 使用知识引导的贪心等价搜索进行因果发现