Semi-Supervised Learning under General Causal Models
This work addresses the challenge of understanding and applying semi-supervised learning in real-world scenarios with flexible causal structures, representing an incremental advancement in causal SSL methods.
The paper tackles the problem of semi-supervised learning under complex causal relationships between features and labels by proposing a framework that works with general causal models, using unlabelled data to learn causal generative models and generate synthetic labelled data, resulting in improved prediction accuracy as verified on simulated and real data.
Semi-supervised learning (SSL) aims to train a machine learning model using both labelled and unlabelled data. While the unlabelled data have been used in various ways to improve the prediction accuracy, the reason why unlabelled data could help is not fully understood. One interesting and promising direction is to understand SSL from a causal perspective. In light of the independent causal mechanisms principle, the unlabelled data can be helpful when the label causes the features but not vice versa. However, the causal relations between the features and labels can be complex in real world applications. In this paper, we propose a SSL framework that works with general causal models in which the variables have flexible causal relations. More specifically, we explore the causal graph structures and design corresponding causal generative models which can be learned with the help of unlabelled data. The learned causal generative model can generate synthetic labelled data for training a more accurate predictive model. We verify the effectiveness of our proposed method by empirical studies on both simulated and real data.