The Digital Causality Lab is a joint initiative by the Chair of Statistics with Application in Business Administration (Prof. Dr. Martin Spindler and Philipp Bach) and the Institute of Logistics, Traffic and Production (Prof. Dr. Knut Haase) at the Faculty of Business Administration at the University of Hamburg.
Prof. Dr. Martin Spindler
Prof. Spindler is professor at the Chair of Statistics with Application in Business Administration at the University of Hamburg. His research focuses on the intersection of machine learning and causal inference, both from a theoretical and an applied point of view. Prof. Spindler gives the lecture “Introduction to Causal Inference” that the DCL accompanies as a practice-oriented lab. Prof. With the DCL, he wants to motivate participants to actively and independently acquire data literacy skills in the context of causality.
Philipp Bach
Philipp Bach is a post-doctoral researcher at the Chair of Statistics with Application in Business Administration at the University of Hamburg. His research focuses on implementations and applications of causal machine learning in economics. Together with Prof. Spindler, he is involved in several statistical software projects like DoubleML. Hence, he wants to teach important skills for working with data. Philipp is in charge of organizing and giving the lab sessions. With the DCL, he wants to experiment with modern ways to teach data science and data literacy skills. You can reach out to Philipp via twitter (@PhilippBach_HH) and visit his profile on GitHub.
Prof. Dr. Knut Haase
Prof. Haase is professor at the Institute of Logistics, Traffic and Production at the University of Hamburg. His research focuses on optimization in transport and logistic and location planing, among others. Prof. Haase has previously launched innovative teaching projects, for example the “Klausurtrainer”, which provided automated tests in the introductory statistics class. With the DCL, he wants to create a new way of teaching that focuses on independent learning and collaboration.
Gangli Tan
Gangli Tan is a research assistant at the Chair of Statistics with Application in Business Administration at the University of Hamburg. She is currently studying for an M.Sc. degree in Business Administration, majoring in Management and Marketing. She is highly interested in causal machine learning, managing AI and digital innovation and transformation in organizations. You can reach out to Gangli via LinkedIn (Gangli Tan). You can also visit her profile on GitHub.