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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.
