Inferring causation from time series in Earth system sciences

The heart of the scientific enterprise is a rational effort to understand the causes behind
the phenomena we observe. In large-scale complex dynamical systems such as the Earth
system, real experiments are rarely feasible. However, a rapidly increasing amount of
observational and simulated data opens up the use of novel data-driven causal methods
beyond the commonly adopted correlation techniques. Here, we give an overview of causal
inference frameworks and identify promising generic application cases common in Earth
system sciences and beyond. We discuss challenges and initiate the benchmark platform
causeme.net to close the gap between method users and developers.