Master Thesis in Causal Machine Learning

Causal inference is one of the major challenges in AI and a core task in many disciplines. The use of synthetic but realistic data is a promising approach as it allows arbitrary parameter settings and the generation of interventional data. During your thesis, you will benchmark the existing causal inference methods. You will develop new causal inference methods and application-driven evaluation metrics. Finally, you will work as part of a global research team in the area of Causal Discovery and Inference, serving different use cases within Bosch. Ideally, the results of your work will be part of a scientific publication.