Identifying causal relationships and quantifying their strength from observational time series data are key problems in disciplines dealing with complex dynamical systems such as the Earth system. Data-driven causal inference in such systems 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 to estimate causal networks from large-scale time series datasets. Our method outperforms state-of-the-art techniques in detection power, which opens up entirely new possibilities to discover and quantify causal networks from time series across a range of research fields. The paper is accompanied by the software package Tigramite. Also have a look at the Perspective paper and causality benchmark platform below!
Nature Communications perspective paper on causal discovery from time series
Together with co-authors, we recently published a Nature Communications perspective paper on causal discovery from time series. The perspective provides an overview of causal inference methods, identifies promising applications, and discusses methodological challenges (exemplified in Earth system sciences). I hope you find it useful for your work!
Watch a talk on the paper at INRIA Saclay here (thanks to Victor Estrade):
Causality benchmark platform www.causeme.net
In addition, we recently launched a causality benchmark platform www.causeme.net. The CauseMe platform provides ground truth benchmark datasets featuring different real challenges to assess and compare the performance of causal discovery methods. As a method developer, you can test your methods. As a method user, you can look up which causal methods are best suited for particular challenges. We need more ground truth datasets, contribute now!