Mapped-PCMCI: an algorithm for causal discovery at the grid level
- 1Deutsches Zentrum für Luft- und Raumfahrt (DLR), Institut für Datenwissenschaften, Jena, Germany (xavier.tibau@dlr.de)
- 2Deutsches Zentrum für Luft- und Raumfahrt (DLR), Institut für Physik der Atmosphäre, Oberpfaffenhofen, Germany
- 3University of Bremen, Institute of Environmental Physics (IUP), Bremen, Germany
- 4Computer Vision Group, Friedrich Schiller University Jena, Jena, Germany
We propose a novel causal discovery method for large-scale gridded time series datasets. Causal discovery has been applied to study a number of problems in climate research in recent years. Causal discovery can be conducted either among spatially aggregated variables (such as modes of climate variability) or by inferring a climate network where the associations among pairs of grid points are treated as a network. In the latter case, causal methods have to deal with several challenges arising from the high dimensionality of such datasets and the data's spatially and temporally redundant nature.
Our method, called Mapped-PCMCI, aims to overcome some of these challenges. The central idea is based on the assumption that there is a lower-dimensional representation of the causal dependencies among different locations. The method first reconstructs a lower-dimensional spatial representation of the data, then conducts causal discovery utilizing the PCMCI method (Runge. et al. 2019), in that lower-dimensional space, and finally maps causal relations back to the grid level. Using spatiotemporal data generated with the spatially aggregated vector-autoregressive (SAVAR) model (Tibau et al. 2020), we demonstrate that Mapped-PCMCI outperforms state-of-the-art methods in orders of magnitude by utilizing the assumption of a lower-dimensional dependency structure. Mapped-PCMCI can be used to better estimate climate networks and thereby help to understand the climate system from the perspective of complex network theory.
J. Runge, P. Nowack, M. Kretschmer, S. Flaxman, D. Sejdinovic, Detecting and quantifying causal associations in large nonlinear time series datasets. Sci. Adv. 5, eaau4996 (2019).
Tibau, X.-A., Reimers, C., Eyring, V., Denzler, J., Reichstein, M., and Runge, J.: Spatiotemporal model for benchmarking causal discovery algorithms, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-9604, https://doi.org/10.5194/egusphere-egu2020-9604, 2020
How to cite: Tibau Alberdi, X.-A., Gerhardus, A., Eyring, V., Denzler, J., and Runge, J.: Mapped-PCMCI: an algorithm for causal discovery at the grid level, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-5633, https://doi.org/10.5194/egusphere-egu21-5633, 2021.
