971 citations · 1.6k across the 23 of their papers we have counts for
23 papers
Causal discovery for time series from multiple datasets with latent contexts
Wiebke Günther, Urmi Ninad, Jakob Runge
Causal discovery from time series data is a typical problem setting across the sciences. Often, multiple datasets of the same system variables are available, for instance, time ser…
Conditional Independence Testing with Heteroskedastic Data and Applications to Causal Discovery
Wiebke Günther, Urmi Ninad, jonas Wahl +1
Conditional independence (CI) testing is frequently used in data analysis and machine learning for various scientific fields and it forms the basis of constraint-based causal disco…
Bootstrap aggregation and confidence measures to improve time series causal discovery
Kevin Debeire, Jakob Runge, Andreas Gerhardus +1
Learning causal graphs from multivariate time series is a ubiquitous challenge in all application domains dealing with time-dependent systems, such as in Earth sciences, biology, o…
Foundations of Causal Discovery on Groups of Variables
Jonas Wahl, Urmi Ninad, Jakob Runge
Discovering causal relationships from observational data is a challenging task that relies on assumptions connecting statistical quantities to graphical or algebraic causal models.…
Discovering Causal Relations and Equations from Data
Gustau Camps-Valls, Andreas Gerhardus, Urmi Ninad +7
Physics is a field of science that has traditionally used the scientific method to answer questions about why natural phenomena occur and to make testable models that explain the p…
Causal Inference on Process Graphs, Part I: The Structural Equation Process Representation
Nicolas-Domenic Reiter, Andreas Gerhardus, Jonas Wahl +1
When dealing with time series data, causal inference methods often employ structural vector autoregressive (SVAR) processes to model time-evolving random systems. In this work, we…
Selecting Robust Features for Machine Learning Applications using Multidata Causal Discovery
Saranya Ganesh S., Tom Beucler, Frederick Iat-Hin Tam +3
Robust feature selection is vital for creating reliable and interpretable Machine Learning (ML) models. When designing statistical prediction models in cases where domain knowledge…
Vector causal inference between two groups of variables
Jonas Wahl, Urmi Ninad, Jakob Runge
Methods to identify cause-effect relationships currently mostly assume the variables to be scalar random variables. However, in many fields the objects of interest are vectors or g…
Causal inference for temporal patterns
Nicolas-Domenic Reiter, Andreas Gerhardus, Jakob Runge
Complex dynamical systems are prevalent in many scientific disciplines. In the analysis of such systems two aspects are of particular interest: 1) the temporal patterns along which…
Towards Learning an Unbiased Classifier from Biased Data via Conditional Adversarial Debiasing
Christian Reimers, Paul Bodesheim, Jakob Runge +1
Bias in classifiers is a severe issue of modern deep learning methods, especially for their application in safety- and security-critical areas. Often, the bias of a classifier is a…
Necessary and sufficient graphical conditions for optimal adjustment sets in causal graphical models with hidden variables
Jakob Runge
The problem of selecting optimal backdoor adjustment sets to estimate causal effects in graphical models with hidden and conditioned variables is addressed. Previous work has defin…
EarthNet2021: A novel large-scale dataset and challenge for forecasting localized climate impacts
Christian Requena-Mesa, Vitus Benson, Joachim Denzler +2
Climate change is global, yet its concrete impacts can strongly vary between different locations in the same region. Seasonal weather forecasts currently operate at the mesoscale (…
A Perspective on Gaussian Processes for Earth Observation
Gustau Camps-Valls, Dino Sejdinovic, Jakob Runge +1
Earth observation (EO) by airborne and satellite remote sensing and in-situ observations play a fundamental role in monitoring our planet. In the last decade, machine learning and…
Discovering contemporaneous and lagged causal relations in autocorrelated nonlinear time series datasets
Jakob Runge
The paper introduces a novel conditional independence (CI) based method for linear and nonlinear, lagged and contemporaneous causal discovery from observational time series in the…
Conditional independence testing based on a nearest-neighbor estimator of conditional mutual information
Jakob Runge
Conditional independence testing is a fundamental problem underlying causal discovery and a particularly challenging task in the presence of nonlinear and high-dimensional dependen…
Detecting causal associations in large nonlinear time series datasets
Jakob Runge, Peer Nowack, Marlene Kretschmer +2
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 possib…
Quantifying information transfer and mediation along causal pathways in complex systems
Jakob Runge
Measures of information transfer have become a popular approach to analyze interactions in complex systems such as the Earth or the human brain from measured time series. Recent wo…
Unified functional network and nonlinear time series analysis for complex systems science: The pyunicorn package
Jonathan F. Donges, Jobst Heitzig, Boyan Beronov +9
We introduce the \texttt{pyunicorn} (Pythonic unified complex network and recurrence analysis toolbox) open source software package for applying and combining modern methods of dat…
Optimal model-free prediction from multivariate time series
Jakob Runge, Reik V. Donner, Jürgen Kurths
Forecasting a time series from multivariate predictors constitutes a challenging problem, especially using model-free approaches. Most techniques, such as nearest-neighbor predicti…
Disentangling different types of El Niño episodes by evolving climate network analysis
Alexander Radebach, Reik V. Donner, Jakob Runge +2
Complex network theory provides a powerful toolbox for studying the structure of statistical interrelationships between multiple time series in various scientific disciplines. In t…
On the graph-theoretical interpretation of Pearson correlations in a multivariate process and a novel partial correlation measure
Jakob Runge
The dependencies of the lagged (Pearson) correlation function on the coefficients of multivariate autoregressive models are interpreted in the framework of time series graphs. Time…
Comment on Wibral et al. (2013): Measuring information-transfer delays, PloS ONE 8(2): e55809
Jakob Runge
Wibral et al. propose a measure of interaction delays rooted in an information-theoretic framework and contrast their measure with the bivariate momentary information transfer (MIT…
Quantifying Causal Coupling Strength: A Lag-specific Measure For Multivariate Time Series Related To Transfer Entropy
Jakob Runge, Jobst Heitzig, Norbert Marwan +1
While it is an important problem to identify the existence of causal associations between two components of a multivariate time series, a topic addressed in Runge et al. (2012), it…