On Causal and Anticausal Learning


Publication Year


Computer Science - Machine Learning,Statistics - Machine Learning


  • Bernhard Schoelkopf
  • Dominik Janzing
  • Jonas Peters
  • Eleni Sgouritsa
  • Kun Zhang
  • Joris Mooij


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.