  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.
