Adaptive machine learning (ML) aims to allow ML models to adapt to ever-changing environments with potential concept drift after model deployment. Traditionally, adaptive ML requires a new dataset to be manually labeled to tailor deployed models to altered data distributions. Recently, an interactive causality based self-labeling method was proposed to autonomously associate causally related data streams for domain adaptation, showing promising results compared to traditional feature similarity-based semi-supervised learning. Several unanswered research questions remain, including self-labeling's compatibility with multivariate causality and the quantitative analysis of the auxiliary models used in the self-labeling. The auxiliary models, the interaction time model (ITM) and the effect state detector (ESD), are vital to the success of self-labeling. This paper further develops the self-labeling framework and its theoretical foundations to address these research questions. A framework for the application of self-labeling to multivariate causal graphs is proposed using four basic causal relationships, and the impact of non-ideal ITM and ESD performance is analyzed. A simulated experiment is conducted based on a multivariate causal graph, validating the proposed theory.

翻译：自适应机器学习旨在使模型在部署后能够适应潜在概念漂移的动态环境。传统自适应方法需要人工标注新数据集，以调整部署模型适应变化的数据分布。近期研究提出了一种基于交互式因果关系的自标注方法，通过自主关联因果相关的数据流实现领域自适应，其效果优于基于特征相似性的传统半监督学习方法。然而仍存在若干待解决问题，包括自标注与多元因果的兼容性及其辅助模型的量化分析。辅助模型（交互时间模型ITM与效果状态检测器ESD）对自标注的成功至关重要。本文进一步拓展了自标注框架及其理论基础以解决上述问题：提出基于四种基本因果关系的多元因果图自标注应用框架，分析了非理想ITM与ESD性能的影响，并通过基于多元因果图的仿真实验验证了所提理论。