Economic Policy and Causal Machine Learning

Economic Policy and Causal Machine Learning

Our goal is to apply recent methodological advances in causal machine learning to improve policy making.

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.

This research receives funding from the Swiss National Science Foundation. The project Causal analysis with Big Data is part of the National Research Programme 75 “Big Data” (NRP 75).
 

Economic impact analysis using Big Data

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Economic impact analysis using Big Data

Project Log

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)

 

Contact

Michael Lechner

Prof. Dr.

Managing Director

SEW-HSG
Büro 06-301
Varnbüelstrasse 14
9000 St. Gallen
Write e-mail
SEW-HSG
Büro 06-301
Varnbüelstrasse 14
9000 St. Gallen
north