Robust Learning via Cause-Effect Models

Abstract

We consider the problem of function estimation in the case where the data distribution may shift between training and test time, and additional information about it may be available at test time. This relates to popular scenarios such as covariate shift, concept drift, transfer learning and semi-supervised learning. This working paper discusses how these tasks could be tackled depending on the kind of changes of the distributions. It argues that knowledge of an underlying causal direction can facilitate several of these tasks.

References in corpus (2)

• Causal Inference on Discrete Data using Additive Noise Models
• Inferring deterministic causal relations

Cited by in corpus (3)

• On Causal and Anticausal Learning
• Causality for Machine Learning
• Latent Causal Invariant Model