Causal inference: A very short intro

27.06.2025, 10:15 - 11:45  –  2.28.0.108
SFB-Kolloquium

Jakob Rung, University of Potsdam

Machine learning excels in learning associations and patterns from data and is increasingly adopted in natural-, life- and social sciences, as well as engineering. However, many relevant research questions about such complex systems are inherently causal and machine learning alone is not designed to answer them. At the same time there often exists ample theoretical and empirical knowledge in the application domains. In this talk, I will briefly outline causal inference as a powerful framework providing the theoretical foundations to combine data and machine learning models with qualitative domain assumptions to quantitatively answer causal questions. I will discuss challenges ahead and selected application scenarios to spark interest for integrating causal thinking into data-driven science. I also look forward to discussing prospects for causal inference in data assimilation.