Causal Inference
Jakob Runge together with Urmi Ninad and Jonas Wahl is teaching a course on “Causal Inference” at TU Berlin in the winter term 2023/24.
Content
In this course you will learn about the modern statistical framework of Causal Inference with a focus on time series. This framework formalizes the notions of cause and effect in mathematical terms, delineates the distinction between mere statistical relationships on the one hand and causal relationships on the other hand, and provides methods that allow to learn and reason about causal relationships in a data-driven way.
The focus of this course in on conveying the framework’s conceptual basics and central concepts. It will also cover a selection of concrete algorithms and applications. A particular emphasis lies on the application of causal inference to time series data, which is ubiquitous in many applied fields.
Literature
- Pearl, J. Causality: Models, reasoning, and inference. Cambridge University Press, 2009
- J. Peters, D. Janzing, and B. Schoelkopf Elements of Causal Inference: Foundations and Learning Algorithms, The MIT Press, Cambridge, Massachusetts, London, England
- Pearl, J., Glymour, M., Jewell, N. P., Causal Inference in Statistics: A Primer. Wiley, 2016.
- Spirtes, P., Glymour, C., and Scheines, R., Causation, Prediction, and Search (MIT Press, Boston, 2000)
- Runge et al. Review article
Schedule
The lecture will be held in English. Please write petra.dudakova@tu-berlin.de if you want to participate. The link will be communicated to all participants enrolled via email.
Contact
In case of any questions please do hesitate to contact Jakob at runge@tu-berlin.de
