用因果模型分析症状对诊断的影响，提升医疗AI的可信度。
Quantifying Symptom Causality in Clinical Decision Making: An Exploration Using CausaLM
- 通过反事实文本生成，量化关键症状的因果影响
- 发现'胸痛'对诊断结果有显著因果作用，超越相关性分析
- 适合医疗AI可解释性研究者和临床决策系统开发者
当前医学诊断的机器学习方法多依赖症状与疾病间的相关性，当症状模糊或跨病种共现时易导致误诊。本文突破相关性局限，聚焦关键症状（如‘胸痛’）的因果影响。基于CausaLM框架，生成目标概念被‘遗忘’的反事实文本表示，从而可量化解构该症状对模型预测疾病分布的因果效应。采用基于文本表征的平均处理效应（TReATE），定量评估症状存在与否如何改变诊断结果，并与基于相关性的基准方法CONEXP对比。结果揭示了临床NLP模型的决策行为本质，为构建更可信、可解释且基于因果的辅助诊断工具提供支持。
原文摘要 · Abstract (English)
Current machine learning approaches to medical diagnosis often rely on correlational patterns between symptoms and diseases, risking misdiagnoses when symptoms are ambiguous or common across multiple conditions. In this work, we move beyond correlation to investigate the causal influence of key symptoms-specifically "chest pain" on diagnostic predictions. Leveraging the CausaLM framework, we generate counterfactual text representations in which target concepts are effectively "forgotten" enabling a principled estimation of the causal effect of that concept on a model's predicted disease distribution. By employing Textual Representation-based Average Treatment Effect (TReATE), we quantify how the presence or absence of a symptom shapes the model's diagnostic outcomes, and contrast these findings against correlation-based baselines such as CONEXP. Our results offer deeper insight into the decision-making behavior of clinical NLP models and have the potential to inform more trustworthy, interpretable, and causally-grounded decision support tools in medical practice.
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