On Causal and Anticausal Learning

Understand

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.