Seminar Causal Reasoning
Causal inference is an important topic in both statistics and computer science and has been applied in various fields from econometrics to neuroscience. In these series of seminars, we will explore classical and recent techniques from causality literature and aim to understand where and how causal reasoning can be useful.
Participants will obtain an overview of relevant topics in causal inference from various fields. They will be able to identify situations in which a dataset can be thought of as a network and analyze it appropriately using a variety of network analytic techniques. Moreover, participants will understand the connections between explainable machine learning and causality.
The emphasis in this seminar is on the independent study of classic papers, as well as recent results in the fields of causal inference and machine learning.
The seminar is primarily aimed at MSc students in Computer Science and adjacent degree programs such as Information Systems, Data Science, or Mathematics. All students have to apply for the seminar.