论文标题
通过非线性变量去评估医疗保健应用的因果推理
Causal Inference via Nonlinear Variable Decorrelation for Healthcare Applications
论文作者
论文摘要
因果推断和模型可解释性研究正在引起人们的关注，尤其是在医疗保健和生物信息学领域。尽管该领域最近取得了成功，但在具有人类可解释表示的非线性环境下取代特征尚未得到充分研究。为了解决这个问题，我们介绍了一种具有可变去相关的新颖方法，以处理线性和非线性混淆。此外，我们采用基于原始特征的关联规则挖掘将关联规则作为新表示，以进一步接近人类决策模式，以提高模型的解释性。大量实验是在四个医疗保健数据集（一个合成生成的，三个关于不同疾病的现实收集的）。与基线方法相比，有关参数估计和因果关系计算的定量结果表明该模型的出色性能。此外，医疗保健专业人员给出的专家评估验证了拟议模型的有效性和解释性。
Causal inference and model interpretability research are gaining increasing attention, especially in the domains of healthcare and bioinformatics. Despite recent successes in this field, decorrelating features under nonlinear environments with human interpretable representations has not been adequately investigated. To address this issue, we introduce a novel method with a variable decorrelation regularizer to handle both linear and nonlinear confounding. Moreover, we employ association rules as new representations using association rule mining based on the original features to further proximate human decision patterns to increase model interpretability. Extensive experiments are conducted on four healthcare datasets (one synthetically generated and three real-world collections on different diseases). Quantitative results in comparison to baseline approaches on parameter estimation and causality computation indicate the model's superior performance. Furthermore, expert evaluation given by healthcare professionals validates the effectiveness and interpretability of the proposed model.
