论文标题
本克：带有神经内核的Beran估计器，用于估计异质治疗效果
BENK: The Beran Estimator with Neural Kernels for Estimating the Heterogeneous Treatment Effect
论文作者
论文摘要
提出了一种估计估计条件平均治疗效果的方法，该方法在审查的事件时间数据（Benk（带有神经内核的Beran估计器））的条件下。该方法背后的主要思想是将Beran估计器应用于估计控制和治疗的生存功能。它不是在Beran估计器中的典型核功能，而是提议以一种称为神经内核的特定形式的神经网络的形式实现内核。通过使用生存功能作为对照和治疗神经网络的结果来估计条件平均治疗效果，该效果由一组具有共享参数的神经内核组成。神经内核更灵活，可以准确地对矢量的复杂位置结构进行建模。各种数值模拟实验说明了本克，并将其与众所周知的T-arearner，S-Sterner和X-Learner进行了比较，用于基于Cox模型，随机生存森林和Nadaraya-Watson回归的几种类型的对照和治疗结果函数。 https://github.com/stasychbr/benk提供了实施Benk的算法代码。
A method for estimating the conditional average treatment effect under condition of censored time-to-event data called BENK (the Beran Estimator with Neural Kernels) is proposed. The main idea behind the method is to apply the Beran estimator for estimating the survival functions of controls and treatments. Instead of typical kernel functions in the Beran estimator, it is proposed to implement kernels in the form of neural networks of a specific form called the neural kernels. The conditional average treatment effect is estimated by using the survival functions as outcomes of the control and treatment neural networks which consists of a set of neural kernels with shared parameters. The neural kernels are more flexible and can accurately model a complex location structure of feature vectors. Various numerical simulation experiments illustrate BENK and compare it with the well-known T-learner, S-learner and X-learner for several types of the control and treatment outcome functions based on the Cox models, the random survival forest and the Nadaraya-Watson regression with Gaussian kernels. The code of proposed algorithms implementing BENK is available in https://github.com/Stasychbr/BENK.
