Inferring causation from time series in Earth system sciences
By Jakob Runge, S. Bathiany, Erik M. Bollt, Gustau Camps-Valls, Dim Coumou, Ethan R. Deyle, Clark Glymour, Marlene Kretschmer, Miguel D. Mahecha, Jordi Muñoz-Marí and 11 more. Nature Communications, 2019
991 citations recorded by the source corpus.
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. Questions of causality are ubiquitous in Earth system sciences and beyond, yet correlation techniques still prevail. This Perspective provides an overview of causal inference methods, identifies promising applications and methodological challenges, and initiates a causality benchmark platform.
Subfields: Artificial Intelligence, Signal Processing
View Paper: https://www.semanticscholar.org/paper/7b983cfa013c532e8316e894ecb78d3cf3aa48b1
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Citations in the corpus
- D’ya Like DAGs? A Survey on Structure Learning and Causal Discovery (2021, methodology): “[139]) user specified adds edges with probability p = 2e d2−d Linear, GP Add, GP Mix, Sigmoid Add and Sigmoid Mix mixed graph data CausalWorld [2] comprehensive robotics dataset MPI3D [71] visual disentanglement dataset Pendulum-light-shadow [257] image data Phase coupled oscillator [7] physical rel”
- Fractional Neuro-Sequential ARFIMA-LSTM for Financial Market Forecasting (2020, background): “…, . . . , p ) and θ i ( i = 1 , 2 , . . . , q ) are autoregressive and moving average parameters ε i is representing white noise with mean zero and variance σ 2 and such a time series with white noise not depending on their previous terms but also depend on other phenomena and other variables [32].”