HyperCausalLP: Causal Link Prediction using Hyper-Relational Knowledge Graph
This work addresses the issue of missing causal links in networks for researchers in causal inference, though it is incremental as it builds on existing knowledge graph link prediction methods by incorporating mediators.
The paper tackles the problem of incomplete causal networks by proposing HyperCausalLP, a method that uses hyper-relational knowledge graphs to predict missing causal links while considering mediator influences, resulting in an average improvement of 5.94% in mean reciprocal rank on the CLEVRER-Humans dataset.
Causal networks are often incomplete with missing causal links. This is due to various issues, such as missing observation data. Recent approaches to the issue of incomplete causal networks have used knowledge graph link prediction methods to find the missing links. In the causal link A causes B causes C, the influence of A to C is influenced by B which is known as a mediator. Existing approaches using knowledge graph link prediction do not consider these mediated causal links. This paper presents HyperCausalLP, an approach designed to find missing causal links within a causal network with the help of mediator links. The problem of missing links is formulated as a hyper-relational knowledge graph completion. The approach uses a knowledge graph link prediction model trained on a hyper-relational knowledge graph with the mediators. The approach is evaluated on a causal benchmark dataset, CLEVRER-Humans. Results show that the inclusion of knowledge about mediators in causal link prediction using hyper-relational knowledge graph improves the performance on an average by 5.94% mean reciprocal rank.