Corpus ID: 17675972

On causal and anticausal learning

@inproceedings{Schlkopf2012OnCA,
  title={On causal and anticausal learning},
  author={B. Sch{\"o}lkopf and D. Janzing and J. Peters and Eleni Sgouritsa and Kun Zhang and J. Mooij},
  booktitle={ICML},
  year={2012}
}
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. 
Causal Transfer Learning
Justifying Information-Geometric Causal Inference
Causal Inference on Discrete Data via Estimating Distance Correlations
Learning Causal Structures Using Regression Invariance
Error asymmetry in causal and anticausal regression
Learning Representations for Counterfactual Inference
Causal discovery with continuous additive noise models
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