Details
| Original language | English |
|---|---|
| Pages (from-to) | 61810-61827 |
| Number of pages | 18 |
| Journal | IEEE ACCESS |
| Volume | 12 |
| Early online date | 29 Apr 2024 |
| Publication status | Published - 7 May 2024 |
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
ASJC Scopus subject areas
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In: IEEE ACCESS, Vol. 12, 07.05.2024, p. 61810-61827.
Research output: Contribution to journal › Article › Research › peer review
}
TY - JOUR
T1 - CauseKG
T2 - A Framework Enhancing Causal Inference With Implicit Knowledge Deduced From Knowledge Graphs
AU - Huang, Hao
AU - Vidal, Maria Esther
N1 - Publisher Copyright: © 2013 IEEE.
PY - 2024/5/7
Y1 - 2024/5/7
N2 - 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.
AB - 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.
KW - Causal inference
KW - knowledge graphs
KW - knowledge reasoning
KW - semantics
UR - http://www.scopus.com/inward/record.url?scp=85192191045&partnerID=8YFLogxK
U2 - 10.1109/ACCESS.2024.3395134
DO - 10.1109/ACCESS.2024.3395134
M3 - Article
AN - SCOPUS:85192191045
VL - 12
SP - 61810
EP - 61827
JO - IEEE ACCESS
JF - IEEE ACCESS
SN - 2169-3536
ER -