Causal Inference is a challenging and exciting research field and developing new methods for fields such as climate science will have a high impact both to advance science and to address climate change topics of critical importance for the society. The group combines innovative methods from different fields (causal inference, nonlinear dynamics, machine learning / deep learning) and closely works with experts in the climate sciences and beyond. One line of research is to address the challenges outlined above (Figure taken from Nature Communications Perspective).

Contribute your own ideas to this exciting field and join for a PhD or Postdoc!

Have a look at the overview article Runge_Causal_Inference_for_Time_Series_NREE from 2023 that covers all aspects of causal inference on time series data. And here’s a recent lecture I gave at as part of the AI4Good seminar series organized by Philip Stier and Duncan Watson-Parris:

Current third-party projects



ERC Starting Grant 2021 — 2025

climate_informatics_logo_big.svgCausalEarth is an interdisciplinary project, aiming to improve our understanding of the causal interdependencies between major drivers (modes) of climate variability by developing novel machine learning-based causal inference methods for both observations and model data. The modes’ interdependencies are characterized by common drivers, indirect effects, nonlinearities, nonstationarity, and all these between highly complex spatio-temporal phenomena. CausalEarth will develop causal inference methods that account for such complex characteristics and apply them to observational and climate model data to improve our understanding of the climate system and climate change.



H2020 PhD training network 2020 — 2023


IMIRACLI is a joint European PhD training network comprising a dozen institutions and industrial partners. The overall goal is to merge AI, machine learning and climate science to deliver a breakthrough in our understanding of the impact of aerosol-cloud interactions on climate. In the Causal Inference group the focus is on method development to address the problem of learning cause and effect relationships from observational data. These methods help to deepen our understanding of complex dynamical processes in the climate system such as the interactions of aerosols and clouds. There are two PhD projects involved, one on causal inference in the presence of multiple time scales and one on causal inference in the presence of latent variables.


H2020 consortium 2021 — 2025


XAIDA, an EU-funded project that started in September 2021, brings together the interdisciplinary expertise of a research consortium of 15 universities and research organizations. Our consortium unites experts in machine learning, statistics and climate modeling. Together we will design new methods and apply them to recent high-impact events to understand the role of climate change. Further, we will study if such events, or even more-intense ones, will occur in the future. We will collaborate with concerned stakeholders from different sectors to prepare risk assessment and adaptation strategies for extreme weather.


Helmholtz AI project 2021 — 2024
helmholtzAICausalFlood is a joint project between DLR and UFZ (Jakob Zscheischler). The overall goal is to understand which physical drivers cause floods which is crucial for flood forecasting and managing flood risk under changing environmental conditions. CausalFlood will advance the development of a state-of-the-art causal inference method to be tailored to the challenges related to causal inference of flood drivers.