HyKG-CF

A Hybrid Approach for Counterfactual Prediction using Domain Knowledge

verfasst von

Hao Huang, Maria Esther Vidal

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.

Organisationseinheit(en)

Forschungszentrum L3S

Externe Organisation(en)

Technische Informationsbibliothek (TIB) Leibniz-Informationszentrum Technik und Naturwissenschaften und Universitätsbibliothek

Typ

Aufsatz in Konferenzband

Seiten

1104-1105

Anzahl der Seiten

2

Publikationsdatum

10.03.2025

Publikationsstatus

Veröffentlicht

Peer-reviewed

Ja

ASJC Scopus Sachgebiete

Computernetzwerke und -kommunikation, Angewandte Informatik, Software

Ziele für nachhaltige Entwicklung

SDG 3 – Gute Gesundheit und Wohlergehen

Elektronische Version(en)

https://doi.org/10.1145/3701551.3708813 (Zugang: Geschlossen)