Details
| Original language | English |
|---|---|
| Title of host publication | WSDM 2025 - Proceedings of the 18th ACM International Conference on Web Search and Data Mining |
| Pages | 1104-1105 |
| Number of pages | 2 |
| ISBN (electronic) | 9798400713293 |
| Publication status | Published - 10 Mar 2025 |
| Event | 18th ACM International Conference on Web Search and Data Mining, WSDM 2025 - Hannover, Germany Duration: 10 Mar 2025 → 14 Mar 2025 |
Abstract
Predictive models are gaining attention as powerful tools for aiding clinicians in diagnosis, prognosis, and treatment recommendations. However, their reliance on associative patterns may raise concerns about reliability of decision support, as association does not necessarily imply causation. To address this limit, we propose HyKG-CF, a hybrid approach to counterfactual prediction that leverages data and domain knowledge encoded in knowledge graph (KG). HyKG-CF integrates symbolic reasoning (on knowledge) with numerical learning (on data) using large language models (LLMs) and statistical models to learn causal Bayesian networks (CBNs) for accurate counterfactual prediction. Using data and knowledge, HyKG-CF improves the accuracy of causal discovery and counterfactual prediction. We evaluate HyKG-CF on a non-small cell lung cancer (NSCLC) KG, demonstrating that it outperforms other baselines. The results highlight the promise of combining domain knowledge with causal models to improve counterfactual prediction.
Keywords
- causality, counterfactual prediction, knowledge graphs
ASJC Scopus subject areas
- Computer Science(all)
- Computer Networks and Communications
- Computer Science(all)
- Computer Science Applications
- Computer Science(all)
- Software
Sustainable Development Goals
Cite this
- Standard
- Harvard
- Apa
- Vancouver
- BibTeX
- RIS
WSDM 2025 - Proceedings of the 18th ACM International Conference on Web Search and Data Mining. 2025. p. 1104-1105.
Research output: Chapter in book/report/conference proceeding › Conference contribution › Research › peer review
}
TY - GEN
T1 - HyKG-CF
T2 - 18th ACM International Conference on Web Search and Data Mining, WSDM 2025
AU - Huang, Hao
AU - Vidal, Maria Esther
N1 - Publisher Copyright: © 2025 Copyright held by the owner/author(s).
PY - 2025/3/10
Y1 - 2025/3/10
N2 - Predictive models are gaining attention as powerful tools for aiding clinicians in diagnosis, prognosis, and treatment recommendations. However, their reliance on associative patterns may raise concerns about reliability of decision support, as association does not necessarily imply causation. To address this limit, we propose HyKG-CF, a hybrid approach to counterfactual prediction that leverages data and domain knowledge encoded in knowledge graph (KG). HyKG-CF integrates symbolic reasoning (on knowledge) with numerical learning (on data) using large language models (LLMs) and statistical models to learn causal Bayesian networks (CBNs) for accurate counterfactual prediction. Using data and knowledge, HyKG-CF improves the accuracy of causal discovery and counterfactual prediction. We evaluate HyKG-CF on a non-small cell lung cancer (NSCLC) KG, demonstrating that it outperforms other baselines. The results highlight the promise of combining domain knowledge with causal models to improve counterfactual prediction.
AB - Predictive models are gaining attention as powerful tools for aiding clinicians in diagnosis, prognosis, and treatment recommendations. However, their reliance on associative patterns may raise concerns about reliability of decision support, as association does not necessarily imply causation. To address this limit, we propose HyKG-CF, a hybrid approach to counterfactual prediction that leverages data and domain knowledge encoded in knowledge graph (KG). HyKG-CF integrates symbolic reasoning (on knowledge) with numerical learning (on data) using large language models (LLMs) and statistical models to learn causal Bayesian networks (CBNs) for accurate counterfactual prediction. Using data and knowledge, HyKG-CF improves the accuracy of causal discovery and counterfactual prediction. We evaluate HyKG-CF on a non-small cell lung cancer (NSCLC) KG, demonstrating that it outperforms other baselines. The results highlight the promise of combining domain knowledge with causal models to improve counterfactual prediction.
KW - causality
KW - counterfactual prediction
KW - knowledge graphs
U2 - 10.1145/3701551.3708813
DO - 10.1145/3701551.3708813
M3 - Conference contribution
AN - SCOPUS:105001669150
SP - 1104
EP - 1105
BT - WSDM 2025 - Proceedings of the 18th ACM International Conference on Web Search and Data Mining
Y2 - 10 March 2025 through 14 March 2025
ER -