Objective:

Root causes of disease intuitively correspond to root vertices of a causal model that increase the likelihood of a diagnosis. This description of a root cause nevertheless lacks the rigorous mathematical formulation needed for the development of computer algorithms designed to automatically detect root causes from data. We seek a definition of patient-specific root causes of disease that models the intuitive procedure routinely utilized by physicians to uncover root causes in the clinic.

Methods:

We use structural equation models, interventional counterfactuals and the recently developed mathematical formalization of backtracking counterfactuals to propose a counterfactual formulation of patient-specific root causes of disease matching clinical intuition.

Results:

We introduce a definition of patient-specific root causes of disease that climbs to the third rung of Pearl’s Ladder of Causation and matches clinical intuition given factual patient data and a working causal model. We then show how to assign a root causal contribution score to each variable using Shapley values from explainable artificial intelligence.

Conclusion:

The proposed counterfactual formulation of patient-specific root causes of disease accounts for noisy labels, adapts to disease prevalence and admits fast computation without the need for counterfactual simulation.

中文翻译：

---

反事实地阐述特定于患者的疾病根本原因

客观的：

疾病的根本原因直观地对应于因果模型的根顶点，从而增加了诊断的可能性。然而，这种对根本原因的描述缺乏开发旨在从数据中自动检测根本原因的计算机算法所需的严格数学公式。我们寻求对特定于患者的疾病根本原因的定义，该定义模拟了医生在临床上常规使用的直观程序来揭示根本原因。

方法：

我们使用结构方程模型、介入性反事实和最近开发的回溯反事实的数学形式化来提出与临床直觉相匹配的患者特定疾病根本原因的反事实表述。

结果：

我们引入了特定于患者的疾病根本原因的定义，该定义上升到珍珠因果关系阶梯的第三级，并与给定实际患者数据和工作因果模型的临床直觉相匹配。然后，我们展示如何使用可解释人工智能的 Shapley 值为每个变量分配根本因果贡献分数。

结论：

所提出的针对特定患者的疾病根本原因的反事实表述可以解释噪声标签，适应疾病流行情况并允许快速计算，而无需反事实模拟。