Causal Inference with applications to time series

We are happy to announce that one of our group members, Andreas Gerhardus, will be teaching a course on “Causal Inference with Applications to Time Series” at the Friedrich-Schiller-Universität Jena in the summer term 2021. The course will be held online and is open for participation.


In this course you will learn about the modern statistical framework of “causal inference”. 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.

Causal inference has in recent years been gaining increasing interest from the machine learning community because models informed by causal knowledge are expected to show better out-of-distribution generalization. Its methods are also applied in various other scientific fields to reason about causal relationships in cases where controlled experimentation is not feasible.

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 ubiquitious in many applied fields.


The course takes places on Thursdays 4pm – 6pm (Central European Summer Time) from April 15th through July 15th. You can still join if you miss the first meeting.

How to participate

FSU students can register directly via Friedolin: https://friedolin.uni-jena.de/qisserver/rds?state=verpublish&status=init&vmfile=no&publishid=187234&moduleCall=webInfo&publishConfFile=webInfo&publishSubDir=veranstaltung. All others are kindly asked to indicate their interest by sending an email to andreas.gerhardus [at] uni-jena.de. The link for joining the meetings will then be communicated privately.


FSU can receive 3 ECTS when successfully passing the examination.