Causal inference for Earth system sciences
The heart of the scientific enterprise is a rational effort to understand the causes behind the phenomena we observe. In disciplines dealing with complex dynamical systems, such as the Earth system, replicated real experiments are rarely feasible. However, a rapidly increasing amount of observational and simulated data opens up the use of novel data-driven causal inference methods beyond the commonly adopted correlation techniques.
This AI for Good Discovery will provide an introduction to the basic principles of causal inference, followed by a series of examples demonstrating how causal inference and discovery have already been used in the Earth sciences. This joint presentation is given by two researchers with a long track record of applying causal inference to applications in Earth system sciences. Finally, it will identify major opportunities, challenges, and corresponding key tasks to be addressed, in order for causal inference methods to further advance the state-of-the-art in Earth system sciences.