The premise of semi-supervised learning (SSL) is that combining labeled and unlabeled data yields significantly more accurate models.

Most recent approaches are motivated heuristically and lack a unifying framework, obscuring connections between them. Further, they often suffer from issues such as being very sensitive to hyperparameters, being expensive to train or needing access to weights hidden behind a closed API.

Specifically, we focus on ergodic Markov chains.

The ability to conduct interventions plays a pivotal role in learning causal relationships among variables, thus facilitating applications across diverse scientific disciplines such as genomics, economics, and machine learning.