The Causal Inference group is part of German Aerospace Center’s Institute of Data Science in Jena (website there). Since 2021 another branch of the group is located at my chair of Climate Informatics at Technische Universität (TU) Berlin as part of an ERC Starting Grant.
For current open positions see here!
UAI workshop on causal inference for time series data
We are happy to announce a workshop on causal inference for time series data at UAI on August 5th. The workshop also features a paper submission track with deadline on June 2! More info on the workshop website here.
New NeurIPS (2022) paper
This paper by Wiebke Günther, Urmi Ninad, Jonas Wahl, and Jakob Runge introduces a partial correlation test for heteroskedastic noise and an associated consistent causal discovery algorithm. Now implemented in Tigramite.
New NeurIPS (2021) paper
This paper solves the problem of finding the optimal adjustment set in causal graphs with hidden variables with minimal (asymptotic) estimation variance.
New NeurIPS (2020) paper
Checkout our new NeurIPS paper on high-recall causal discovery for autocorrelated time series with latent confounders. Joint work with Andreas Gerhardus. More comparisons soon on the causality benchmark platform http://causeme.net
New UAI (2020) paper
Checkout my new UAI paper on time series causal discovery for lagged AND contemporaneous dependencies (PCMCI+). Often causal links have a shorter time lag than the resolution of the time series, which leads to contemporaneous links that cannot be directed using methods like Granger causality and also my previous method PCMCI (Science Advances paper below). PCMCI+ addresses this issue and can identify contemporaneous causal directions even between just two time series, if any of them has enough autocorrelation. Works very well also for many variables. More comparisons soon on the causality benchmark platform http://causeme.net
New Science Advances paper on causal discovery
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!