Causal networks are useful in a wide variety of applications, from medical diagnosis to root-cause analysis in manufacturing. In practice, however, causal networks are often incomplete with missing causal relations. This paper presents a novel approach, called CausalLP, that formulates the issue of incomplete causal networks as a knowledge graph completion problem. More specifically, the task of finding new causal relations in an incomplete causal network is mapped to the task of knowledge graph link prediction. The use of knowledge graphs to represent causal relations enables the integration of external domain knowledge; and as an added complexity, the causal relations have weights representing the strength of the causal association between entities in the knowledge graph. Two primary tasks are supported by CausalLP: causal explanation and causal prediction. An evaluation of this approach uses a benchmark dataset of simulated videos for causal reasoning, CLEVRER-Humans, and compares the performance of multiple knowledge graph embedding algorithms. Two distinct dataset splitting approaches are used for evaluation: (1) random-based split, which is the method typically employed to evaluate link prediction algorithms, and (2) Markov-based split, a novel data split technique that utilizes the Markovian property of causal relations. Results show that using weighted causal relations improves causal link prediction over the baseline without weighted relations.

翻译：因果网络在从医学诊断到制造业根因分析等广泛领域中具有重要应用价值。然而在实践中，因果网络往往存在因果关系缺失的不完整问题。本文提出了一种名为CausalLP的新方法，将不完整因果网络问题转化为知识图谱补全任务。具体而言，该方法将从不完整因果网络中发掘新因果关系的任务映射为知识图谱链接预测问题。利用知识图谱表示因果关系能够整合外部领域知识；作为额外的复杂性，因果关系具有表示知识图谱中实体间因果关联强度的权重。CausalLP主要支持两项任务：因果解释与因果预测。本研究使用因果推理模拟视频基准数据集CLEVRER-Humans进行评估，并比较了多种知识图谱嵌入算法的性能。评估采用两种不同的数据集划分方法：（1）基于随机划分的方法，这是评估链接预测算法的典型方法；（2）基于马尔可夫划分的方法，这是一种利用因果关系马尔可夫性质的新型数据划分技术。实验结果表明，相较于未加权关系的基线方法，使用加权因果关系能有效提升因果链接预测性能。