Causal discovery in climate research: Overview and recent progress

Discovering causal dependencies from observational time series datasets is a major problem in better understanding the complex dynamical system Earth. Recent methodological advances have addressed major challenges such as high-dimensionality and nonlinearity (PCMCI, Runge et al. Sci. Adv. 2019), as well as instantaneous causal links (PCMCI+, Runge UAI, 2020) and hidden variables (LPCMCI, Gerhardus and Runge, 2020), but many more remain. In this presentation I will give an overview of challenges and methods and present a recent approach, Ensemble-PCMCI, to analyze ensembles of climate time series. An example for this are initialized ensemble forecasts. Since the individual samples can then be created from several time series instead of different time steps from a single time series, such cases allow to relax the assumption of stationarity and hence to analyze whether and how the underlying causal relationships change over time. We compare Ensemble-PCMCI to other methods and discuss preliminary applications.

Runge et al., Detecting and quantifying causal associations in large nonlinear time series datasets, Science Advances eeaau4996 (2019).

Runge, J. Discovering contemporaneous and lagged causal relations in autocorrelated nonlinear time series datasets. Proceedings of the 36th Conference on Uncertainty in Artificial Intelligence, UAI 2020, Toronto, Canada, 2019, AUAI Press, 2020

Gerhardus, A. & Runge, J. High-recall causal discovery for autocorrelated time series with latent confounders. Advances in Neural Information Processing Systems, 2020, 33