  We consider the problem of function estimation in the case where an
underlying causal model can be inferred. This has implications for popular
scenarios such as covariate shift, concept drift, transfer learning and
semi-supervised learning. We argue that causal knowledge may facilitate some
approaches for a given problem, and rule out others. In particular, we
formulate a hypothesis for when semi-supervised learning can help, and
corroborate it with empirical results.
