论文标题
通过异性噪声模型识别患者特定的根本原因
Identifying Patient-Specific Root Causes with the Heteroscedastic Noise Model
论文作者
论文摘要
复杂的疾病是由许多因素引起的，即使在同一诊断类别中，患者之间可能有所不同。然而，一些潜在的根本原因可能会引发每个患者内部疾病的发展。因此，我们专注于鉴定患者特异性的疾病根本原因，我们将其等同于结构方程模型中外源误差项的样本特异性预测性。我们从线性设置概括到异质机噪声模型，其中$ y = m（x） + \varepsilonσ（x）$具有非线性函数$ m（x）$和$σ（x）$，分别代表条件平均值和平均值偏差。该模型保留了可识别性，但引入了非平凡的挑战，该挑战需要一种称为广义根因果推理（GRCI）的自定义算法才能正确提取错误项。 GRCI比现有替代方案更准确地恢复了患者特定的根部。
Complex diseases are caused by a multitude of factors that may differ between patients even within the same diagnostic category. A few underlying root causes may nevertheless initiate the development of disease within each patient. We therefore focus on identifying patient-specific root causes of disease, which we equate to the sample-specific predictivity of the exogenous error terms in a structural equation model. We generalize from the linear setting to the heteroscedastic noise model where $Y = m(X) + \varepsilonσ(X)$ with non-linear functions $m(X)$ and $σ(X)$ representing the conditional mean and mean absolute deviation, respectively. This model preserves identifiability but introduces non-trivial challenges that require a customized algorithm called Generalized Root Causal Inference (GRCI) to extract the error terms correctly. GRCI recovers patient-specific root causes more accurately than existing alternatives.
