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On causal and anticausal learning

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Schölkopf,  B
Dept. Empirical Inference, Max Planck Institute for Intelligent Systems, Max Planck Society;

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Janzing,  D
Dept. Empirical Inference, Max Planck Institute for Intelligent Systems, Max Planck Society;

/persons/resource/persons84134

Peters,  J
Dept. Empirical Inference, Max Planck Institute for Intelligent Systems, Max Planck Society;

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Sgouritsa,  E
Dept. Empirical Inference, Max Planck Institute for Intelligent Systems, Max Planck Society;

/persons/resource/persons84328

Zhang,  K
Dept. Empirical Inference, Max Planck Institute for Intelligent Systems, Max Planck Society;

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Citation

Schölkopf, B., Janzing, D., Peters, J., Sgouritsa, E., Zhang, K., & Mooij, J. (2012). On causal and anticausal learning. In J. Langford, & J. Pineau (Eds.), 29th International Conference on Machine Learning (ICML 2012) (pp. 1255-1262). Madison, WI, USA: International Machine Learning Society.


Cite as: http://hdl.handle.net/11858/00-001M-0000-0013-B6C6-9
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.