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.
Since Galileo Galilei, insight into the causes behind the phenomena we observe has come from two strands of modern science: observational discoveries and carefully designed experiments that intervene in the system of interest under well-controlled conditions.
Fortunately, recent decades have seen an explosion in the availability of large-scale time series data, both from observations (satellite remote sensing, station-based, or field site measurements), and from Earth system model outputs.
Such data repositories, together with increasing computational power, open up novel ways to use data-driven methods for the alternative strand of modern science: observational causal discoveries.
In contrast to data-driven machine learning methods such as probabilistic modeling, kernel machines, or in particular deep learning, which mainly focus on prediction and classification, causal inference methods aim at discovering and quantifying the causal interdependencies of the underlying system.
Causal inference methods do have the potential to substantially advance the state-of-the-art — if the underlying assumptions and methodological challenges are taken into consideration:
- Causal hypothesis testing
- Causal complex network analysis
- Exploratory detection of causes of extreme impacts
- Causal evaluation of physical models
- Process challenges
- Data challenges
- Computational and statistical challenges
The benchmark platform causeme.net closes the gap between method users and developers.
Applying and interpreting causal inference methods and integrating these with physical modeling, however, will also require more in-depth training on methods in Earth system sciences.
Moreover, data-driven causality analyses need to be designed carefully: They should be guided by expert knowledge of the system (requiring expertise from the relevant field) and interpreted based on the assumptions and limitations of the causality method used (requiring expertise from the causal inference method). Sensibly applied causal inference methods promise to substantially advance the state-of-the-art in understanding complex dynamical systems from data also in many other fields with similar challenges as in Earth system sciences, if domain scientists and method developers closely work together—and join the ‘causal revolution’.