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
At the process level,
autocorrelation (1), time delays (2), and nonlinearity (3), also in the form of state-dependence and synergistic behavior (4), require a careful selection of the estimation method. Further, a time series might contain signals from different processes acting on vastly different time scales (5). Noise distributions (6) can feature heavy tails and extreme-values which challenges the ubiquitous methodological Gaussian assumption.
At the data aggregation level,
the most basic challenge is the definition of the causally relevant variables (7) representing the subprocesses of interest from spatio-temporally gridded data (e.g., from satellites) or station data measurements. Unobserved variables (8) need to be taken into account regarding a causal interpretation of the estimated graph. Time sub-sampling (9) and aggregation (10) can make causal links appear contemporaneous and even cyclic due to insufficient time resolution (e.g., due to the standard practice of time averaging depicted here in a time series graph24). Causal inferences are degraded due to measurement errors (11) such as observational noise, systematic biases (first few samples), or even missing values (grey samples), that may be causally related to the measured process, constituting a form of selection bias (12). Some datasets are of a discrete type (13), either due to quantization, or as categorical data, e.g., an index representing different weather regimes, and require methods that deal with discrete, and also mixed data types. Next to measurement value uncertainties, for paleo-climatic data even the measurement time points typically are given only with uncertainty (14), which especially challenges methods exploiting time-order.
At the computational and statistical level,
the scalability of methods, regarding both sample size (15) and high dimensionality (16) due to the number of variables as well as large time delays, is of crucial practical relevance for computational run-time and detection power. Finally, uncertainty estimation (17, width of links), also taking into account data uncertainties, poses a major challenge
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’.
Surtil : 