Teaching

Causal Inference

Jakob Runge will be teaching a course on “Causal Inference” at TU Berlin in the winter term 2021/22. The course will be held online and is open for participation.

Content

You will learn basic concepts of causality according to Pearl and Spirtes and the corresponding mathematical apparatus. Starting from the concept of graphical models and conditional independence, learning algorithms are discussed. An important part is the practical estimation problem for learning causal graphs and statistical methods. Since many applied sciences involve time series data, special emphasis will be given to problems characteristic of time series. We will conclude with exemplary applications of causal inference methods to climate time series.

Literature

Pearl, J. Causality: Models, reasoning, and inference. Cambridge University Press, 2009
Spirtes, P., Glymour, C., and Scheines, R., Causation, Prediction, and Search (MIT Press, Boston, 2000)
Runge et al. Perspective article: https://www.nature.com/articles/s41467-019-10105-3

Schedule

The courses take place online. The first meeting is Friday, Oct 22nd 15:15-16:45, but there will be a flexible schedule since I am not available every Friday. You can still join if you miss the first meeting.

Link: https://meet.jit.si/tu_berlin_causal_inference