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.

Short bio: Jakob Runge heads the Causal Inference group at the German Aerospace Center’s Institute of Data Science in Jena since 2017 and is guest professor of computer science at TU Berlin since 2021. His group develops theory, methods, and accessible software for causal inference on time series data inspired by challenges in various application domains. Jakob studied physics at Humboldt University Berlin and finished his PhD project at the Potsdam Institute for Climate Impact Research in 2014. For his studies he was funded by the German National Foundation (Studienstiftung) and his thesis was awarded the Carl-Ramsauer prize by the Berlin Physical Society. In 2014 he won a $200.000 Fellowship Award in Studying Complex Systems by the James S. McDonnell Foundation and joined the Grantham Institute, Imperial College London, from 2016 to 2017. In 2020 he won an ERC Starting Grant with his interdisciplinary project CausalEarth. On https://github.com/jakobrunge/tigramite.git he provides Tigramite, a time series analysis python module for causal inference. For more details, see: www.climateinformaticslab.com.