CauseKG

A Framework Enhancing Causal Inference With Implicit Knowledge Deduced From Knowledge Graphs

authored by
Hao Huang, Maria Esther Vidal
Abstract

Causal inference is a critical technique for inferring causal relationships from data and distinguishing causation from correlation. Causal inference frameworks rely on structured data, typically represented in flat tables or relational models. These frameworks estimate causal effects based only on explicit facts, overlooking implicit information in the data, which can lead to inaccurate causal estimates. Knowledge graphs (KGs) inherently capture implicit information through logical rules applied to explicit facts, providing a unique opportunity to leverage implicit knowledge. However, existing frameworks are not applicable to KGs due to their semi-structured nature. CauseKG is a causal inference framework designed to address the intricacies of KGs and seamlessly integrate implicit information using KG-specific entailment techniques, providing a more accurate causal inference process. We empirically evaluate the effectiveness of CauseKG against benchmarks constructed from synthetic and real-world datasets. The results suggest that CauseKG can produce a lower mean absolute error in causal inference compared to state-of-the-art methods. The empirical results demonstrate CauseKG's ability to address causal questions in a variety of domains. This research highlights the importance of extending causal inference techniques to KGs, emphasising the improved accuracy that can be achieved by integrating implicit and explicit information.

Organisation(s)
L3S Research Centre
External Organisation(s)
German National Library of Science and Technology (TIB)
Type
Article
Journal
IEEE ACCESS
Volume
12
Pages
61810-61827
No. of pages
18
ISSN
2169-3536
Publication date
07.05.2024
Publication status
Published
Peer reviewed
Yes
ASJC Scopus subject areas
General Computer Science, General Materials Science, General Engineering
Electronic version(s)
https://doi.org/10.1109/ACCESS.2024.3395134 (Access: Open)