CMStatistics 2017: Start Registration
View Submission - CMStatistics
Title: Large-scale causal discovery from nonlinear time series datasets Authors:  Jakob Runge - Imperial College London (United Kingdom) [presenting]
Abstract: Detecting causal associations in observational time series datasets is a key challenge for novel insights into complex dynamical systems such as the Earth system. Dependencies in such a high-dimensional dynamical system may involve time-delays, nonlinearity, and strong autocorrelations, which present major challenges for causal discovery techniques. Here we are interested in time-lagged causal discovery using conditional independence testing and address two major problems: (1) Low power due to high-dimensionality and (2) detection `biases'. The latter refers to the problem that the detection power for individual links may depend not only on their causal strength, but also on autocorrelation and other dependencies. We introduce a method for large-scale, linear and nonlinear, time-delayed causal discovery. In extensive numerical experiments we find that our method yields more power than common methods and largely overcomes detection biases allowing to more accurately rank associations in large-scale analyses by their causal strength. We demonstrate the method on a large-scale climate time series dataset.