Title: Detecting causal associations in large nonlinear time series datasets,
Abstract: Identifying causal relationships from observational time series data is a key
problem in disciplines such as climate science or neuroscience, where
experiments are often not possible. Data-driven causal inference is challenging
since datasets are often high-dimensional and nonlinear with limited sample
sizes. Here we introduce a novel method that flexibly combines linear or
nonlinear conditional independence tests with a causal discovery algorithm that
allows to reconstruct causal networks from large-scale time series datasets. We
validate the method on a well-established climatic teleconnection connecting
the tropical Pacific with extra-tropical temperatures and using large-scale
synthetic datasets mimicking the typical properties of real data. The
experiments demonstrate that our method outperforms alternative techniques in
detection power from small to large-scale datasets and opens up entirely new
possibilities to discover causal networks from time series across a range of
research fields.