HyperCausalLP: Causal Link Prediction using Hyper-Relational Knowledge Graph

TL;DR

HyperCausalLP introduces a hyper-relational knowledge graph method to improve causal link prediction by incorporating mediators, significantly enhancing accuracy on benchmark datasets.

Contribution

The paper proposes a novel hyper-relational knowledge graph approach for causal link prediction that explicitly models mediators, addressing limitations of existing methods.

Findings

Improves mean reciprocal rank by 5.94% on CLEVRER-Humans.

Effectively models mediated causal links in knowledge graphs.

Demonstrates superior performance over existing link prediction methods.

Abstract

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.