What is it about?

This paper introduces a novel method called HyKG-CF that aims to improve decision-making in healthcare by answering “what if” questions more accurately. In simple terms, the paper is about predicting what might happen if a patient’s situation were different—for example, what would be the effect if a lung cancer patient were a non-smoker instead of a smoker. Traditional medical prediction models mainly rely on spotting patterns in data. However, these models often miss the underlying cause-and-effect relationships, which are crucial for making informed treatment decisions. HyKG-CF addresses this gap by combining two key approaches: 1. Data-Driven Analysis: It uses statistical methods to learn from patient data and identify correlations. 2. Knowledge-Driven Reasoning: It incorporates domain-specific knowledge stored in healthcare knowledge graphs. These graphs map out the relationships between different medical factors (like patient characteristics and treatment details), providing a deeper understanding of causality. By fusing these approaches, HyKG-CF builds a causal model that not only predicts outcomes but also explains the “why” behind them. For instance, using real-world data from non-small cell lung cancer cases, the method can simulate interventions (such as a change in smoking status) and predict the likely changes in outcomes. This makes the model more transparent and trustworthy for clinicians and decision-makers. Overall, the paper is about enhancing the reliability and interpretability of predictive models in medicine. It offers a way to bridge the gap between complex data analysis and practical, understandable insights, which is essential for effective healthcare decisions.

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Why is it important?

This method overcomes the pitfalls of traditional causal discovery methods: misidentifying causal edges due to lacking of understanding of variables; incompleted causal graph due to bias in data. Our method, HyKG-CF, utilizes the domain knowledge (e.g., from medical area) via large language model, and the data, can lead to a more robust and accurate causal discovery, therefore enhancing the performance of the counterfactual prediction.

Perspectives

I hope this article can rise attention of researchers on the power of Hybrid methods (symbolic and subsymbolic), in our method, we use the symbolic part (LLM for undersding meaning of variables) to combine with subsymbolic part (data-driven methods for mining patterns from data). However, the difficulty of obtaining ground truth for causal inference (cuasal discovery, causal estimation, and counterfactual prediction) on real world is still chanllenging.

Huang Hao

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This page is a summary of: HyKG-CF: A Hybrid Approach for Counterfactual Prediction using Domain Knowledge, March 2025, ACM (Association for Computing Machinery),
DOI: 10.1145/3701551.3708813.
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