The availability of “Big Data” and the abundance of computing power have triggered the development of new methods for causal analysis based on machine learning. Our research interest is to explore the potential of these causal machine learning methods and make them applicable to real-world policy questions. They can guide optimal policy decisions and help to systematically evaluate existing policy measure.
|September 2021||Completed Project - Interview with Michael Lechner|
|July 2019||Call for papers for the workshop on Causal Machine Learning to be held in January 2020|
|January 2019||Blog entry about personalized caual effects posted at Big Data Dialog|
|November 2018||Interview with Michael Lechner regarding the progress of the project on the Big Data Dialog|
|February 2018||Public communication in the magazine HSG Focus of the University of St.Gallen, "The Allocation of Training Programmes for Unemployed Persons" by Anthony Strittmatter|
|May 9th, 2017||Kick-off NRP75 in Berne (Poster, One Minute Madness Presentation)|