  We consider the problem of function estimation in the case where the data
distribution may shift between training and test time, and additional
information about it may be available at test time. This relates to popular
scenarios such as covariate shift, concept drift, transfer learning and
semi-supervised learning. This working paper discusses how these tasks could be
tackled depending on the kind of changes of the distributions. It argues that
knowledge of an underlying causal direction can facilitate several of these
tasks.
