The focus of this study is to evaluate the effectiveness of Machine Learning (ML) methods for two-sample testing with right-censored observations. To achieve this, we develop several ML-based methods with varying architectures and implement them as two-sample tests. Each method is an ensemble (stacking) that combines predictions from classical two-sample tests. This paper presents the results of training the proposed ML methods, examines their statistical power compared to classical two-sample tests, analyzes the null distribution of the proposed methods when the null hypothesis is true, and evaluates the significance of the features incorporated into the proposed methods. In total, this work covers 18 methods for two-sample testing under right-censored observations, including the proposed methods and classical well-studied two-sample tests. All results from numerical experiments were obtained from a synthetic dataset generated using the inverse transform sampling method and replicated multiple times through Monte Carlo simulation. To test the two-sample problem with right-censored observations, one can use the proposed two-sample methods (scripts, dataset, and models are available on GitHub and Hugging Face).

翻译：本研究旨在评估机器学习方法在右删失观测数据下进行两样本检验的有效性。为此，我们开发了多种不同架构的基于机器学习的方法，并将其实现为两样本检验。每种方法均为集成（堆叠）模型，其结合了经典两样本检验的预测结果。本文展示了所提出的机器学习方法的训练结果，比较了它们与经典两样本检验的统计功效，分析了在零假设成立时这些方法的零分布，并评估了所纳入特征的重要性。总体而言，本研究涵盖了右删失观测下进行两样本检验的18种方法，包括所提出的方法和经典的、经过充分研究的两样本检验。所有数值实验结果均基于使用逆变换采样方法生成的合成数据集获得，并通过蒙特卡洛模拟进行了多次重复。对于右删失观测下的两样本问题，研究者可使用本文提出的两样本检验方法（相关脚本、数据集及模型已在GitHub和Hugging Face平台开源）。