Many scientific questions are fundamentally about cause and effect. Causal inference offers a principled way to combine data with domain knowledge to answer such questions, supported by theory and tools from statistics, machine learning, and AI. The Causal Inference Lab’s mission is to develop theory, methods, and accessible tools for causal inference on time series data. Since March 2025, the lab is based at University of Potsdam’s chair of AI in the Sciences, held by Prof. Dr. Jakob Runge. Since 2021, a second branch of the group has been active at Technische Universität (TU) Berlin through an ERC Starting Grant.
News
Tigramite Overview Talk
Here’s a recent talk on the Tigramite package for Causal Inference for Time Series Data that was part of the regular Online Causal Inference Seminar series.
Nature Reviews Earth and Environment Technical Review Paper
In this Technical Review, we explain the use of causal inference frameworks with a focus on the challenges of time series data. Domain- adapted explanations, method guidance, and practical case studies provide an accessible summary of methods for causal discovery and causal effect estimation.
DLR Science Award 2022
I am very honored to have received the 2022 DLR science award for my work on optimal causal effect estimation. For a video (in German) 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!
