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)

BibTeX

@article{a8966c39c04b472bb73febde143fc0c0,
title = "CauseKG: A Framework Enhancing Causal Inference With Implicit Knowledge Deduced From Knowledge Graphs",
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.",
keywords = "Causal inference, knowledge graphs, knowledge reasoning, semantics",
author = "Hao Huang and Vidal, {Maria Esther}",
note = "Publisher Copyright: {\textcopyright} 2013 IEEE.",
year = "2024",
month = may,
day = "7",
doi = "10.1109/ACCESS.2024.3395134",
language = "English",
volume = "12",
pages = "61810--61827",
journal = "IEEE ACCESS",
issn = "2169-3536",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
}