Causal discovery is a process of discovering new causal relations from
observational data. Traditional causal discovery methods often suffer from
issues related to missing data To address these issues, this paper presents a
novel approach called CausalDisco that formulates causal discovery as a
knowledge graph completion problem. More specifically, the task of discovering
causal relations is mapped to the task of knowledge graph link prediction.
CausalDisco supports two types of discovery: causal explanation and causal
prediction. The causal relations have weights representing the strength of the
causal association between entities in the knowledge graph. 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. In addition, two distinct dataset splitting
approaches are utilized within the evaluation: (1) random-based split, which is
the method typically used to evaluate link prediction algorithms, and (2)
Markov-based split, a novel data split technique for evaluating link prediction
that utilizes the Markovian property of the causal relation. Results show that
using weighted causal relations improves causal discovery over the baseline
without weighted relations.

CausalDisco 是一种将因果关系发现作为知识图谱补全问题的新方法，支持因果解释和因果预测，并通过比较多个知识图嵌入算法在模拟视频数据集上的表现，证明了使用加权因果关系能够提高因果关系发现的性能。