On Causal and Anticausal Learning
Abstract
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.
Cite
Text
Schölkopf et al. "On Causal and Anticausal Learning." International Conference on Machine Learning, 2012.Markdown
[Schölkopf et al. "On Causal and Anticausal Learning." International Conference on Machine Learning, 2012.](https://mlanthology.org/icml/2012/scholkopf2012icml-causal/)BibTeX
@inproceedings{scholkopf2012icml-causal,
title = {{On Causal and Anticausal Learning}},
author = {Schölkopf, Bernhard and Janzing, Dominik and Peters, Jonas and Sgouritsa, Eleni and Zhang, Kun and Mooij, Joris M.},
booktitle = {International Conference on Machine Learning},
year = {2012},
url = {https://mlanthology.org/icml/2012/scholkopf2012icml-causal/}
}