Toward Causal Machine Learning - Prof. Bernhard Schölkopf

Yandex School of Data Analysis Conference
Machine Learning: Prospects and Applications

https://yandexdataschool.com/conference

In machine learning, we use data to automatically find dependences in
the world, with the goal of predicting future observations. Most machine
learning methods build on statistics, but one can also try to go beyond
this, assaying causal structures underlying statistical dependences. Can
such causal knowledge help prediction in machine learning tasks? We
argue that this is indeed the case, due to the fact that causal models are
more robust to changes that occur in real world datasets. We touch upon
the implications of causal models for machine learning tasks such as domain
adaptation, transfer learning, and semi-supervised learning. We also
present an application to the removal of systematic errors for the purpose
of exoplanet detection.

Machine learning currently mainly focuses on relatively well-studied
statistical methods. Some of the causal problems are conceptually harder,
however, the causal point of view can provide additional insights that have
substantial potential for data analysis.