On Causal and Anticausal Learning
This work addresses the challenge of improving learning algorithms in dynamic or shifted environments, such as transfer learning, by incorporating causal insights, though it appears incremental in applying causal theory to existing problems.
The paper tackles the problem of function estimation by leveraging underlying causal models, showing that causal knowledge can guide approaches in scenarios like covariate shift and semi-supervised learning, with empirical results supporting a hypothesis on when semi-supervised learning is effective.
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.