Self-Labeling in Multivariate Causality and Quantification for Adaptive
  Machine Learning
Yutian Ren Aaron Haohua Yen G. P. Li
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.