论文标题
机器学习生存模型的反事实解释
Counterfactual explanation of machine learning survival models
论文作者
论文摘要
提出了一种对机器学习生存模型的反事实解释的方法。解决反事实解释问题的困难之一是，以生存功能的形式通过机器学习生存模型的结果隐式定义了示例类别。引入了原始示例的生存函数与反事实的生存函数之间的差异。此条件基于使用平均时间与事件之间的距离。结果表明，当解释的黑盒模型是COX模型时，可以将反事实解释问题通过线性约束简化为标准凸优化问题。对于其他黑盒模型，建议应用众所周知的粒子群优化算法。实际和合成数据的许多数值实验都证明了所提出的方法。
A method for counterfactual explanation of machine learning survival models is proposed. One of the difficulties of solving the counterfactual explanation problem is that the classes of examples are implicitly defined through outcomes of a machine learning survival model in the form of survival functions. A condition that establishes the difference between survival functions of the original example and the counterfactual is introduced. This condition is based on using a distance between mean times to event. It is shown that the counterfactual explanation problem can be reduced to a standard convex optimization problem with linear constraints when the explained black-box model is the Cox model. For other black-box models, it is proposed to apply the well-known Particle Swarm Optimization algorithm. A lot of numerical experiments with real and synthetic data demonstrate the proposed method.
