A new meta-algorithm for estimating the conditional average treatment effects is pro-posed in the paper. The basic idea behind the algorithm is to consider a new dataset consisting of feature vectors produced by means of concatenation of examples from control and treatment groups, which are close to each other. Outcomes of new data are defined as the difference between outcomes of the corresponding examples comprising new feature vectors. The second idea is based on the assumption that the number of controls is rather large and the control outcome function is precisely determined. This assumption allows us to augment treatments by generating feature vectors which are closed to available treatments. The outcome regression function constructed on the augmented set of concatenated feature vectors can be viewed as an estimator of the conditional average treatment effects. A simple modification of the Co-learner based on the random subspace method or the feature bagging is also proposed. Various numerical simulation experiments illustrate the proposed algorithm and show its outperformance in comparison with the well-known T-learner and X-learner for several types of the control and treatment outcome functions.

中文翻译：

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使用特征向量的连接和增强估计个性化异构治疗效果

本文提出了一种新的估计条件平均治疗效果的元算法。该算法背后的基本思想是考虑一个新的数据集，该数据集由通过连接来自控制组和治疗组的示例产生的特征向量组成，这些样本彼此接近。新数据的结果被定义为包含新特征向量的相应示例的结果之间的差异。第二个想法是基于控制的数量相当大并且控制结果函数被精确确定的假设。这个假设允许我们通过生成与可用治疗接近的特征向量来增强治疗。在连接特征向量的增强集上构建的结果回归函数可以被视为条件平均治疗效果的估计量。还提出了基于随机子空间方法或特征套袋的 Co-learner 的简单修改。各种数值模拟实验说明了所提出的算法，并在几种类型的控制和治疗结果函数方面显示了其优于著名的 T 学习器和 X 学习器的性能。