Quantifying Causal Coupling Strength: A Lag-specific Measure For Multivariate Time Series Related To Transfer Entropy - Jakob Runge; Jobst Heitzig; Norbert Marwan; Jürgen Kurths (CoRR, 2012)

Statistical Mechanics and Information-Theoretic Perspectives on Complexity in the Earth System. - Georgios Balasis; Reik V. Donner; Stelios M. Potirakis; Jakob Runge; Constantinos Papadimitriou; Ioannis A. Daglis; Konstantinos Eftaxias; Jürgen Kurths (Entropy, 2013)

Reliability of Inference of Directed Climate Networks Using Conditional Mutual Information. - Jaroslav Hlinka; David Hartman; Martin Vejmelka; Jakob Runge; Norbert Marwan; Jürgen Kurths; Milan Palus (Entropy, 2013)

Conditional independence testing based on a nearest-neighbor estimator of conditional mutual information. - Jakob Runge (AISTATS, 2018)

Nonlinear Causal Link Estimation Under Hidden Confounding with an Application to Time Series Anomaly Detection. - Violeta Teodora Trifunov; Maha Shadaydeh; Jakob Runge; Veronika Eyring; Markus Reichstein; Joachim Denzler (GCPR, 2019)

The Causality for Climate Competition. - Jakob Runge; Xavier-Andoni Tibau; Matthias Bruhns; Jordi Muñoz-Marí; Gustau Camps-Valls (NeurIPS (Competition and Demos), 2019)

Determining the Relevance of Features for Deep Neural Networks. - Christian Reimers; Jakob Runge; Joachim Denzler (ECCV (26), 2020)

High-recall causal discovery for autocorrelated time series with latent confounders. - Andreas Gerhardus; Jakob Runge (NeurIPS, 2020)

Discovering contemporaneous and lagged causal relations in autocorrelated nonlinear time series datasets. - Jakob Runge (UAI, 2020)

A Perspective on Gaussian Processes for Earth Observation. - Gustau Camps-Valls; Dino Sejdinovic; Jakob Runge; Markus Reichstein (CoRR, 2020)

EarthNet2021: A novel large-scale dataset and challenge for forecasting localized climate impacts. - Christian Requena-Mesa; Vitus Benson; Joachim Denzler; Jakob Runge; Markus Reichstein (CoRR, 2020)

Conditional Dependence Tests Reveal the Usage of ABCD Rule Features and Bias Variables in Automatic Skin Lesion Classification. - Christian Reimers; Niklas Penzel; Paul Bodesheim; Jakob Runge; Joachim Denzler (CVPR Workshops, 2021)

EarthNet2021: A Large-Scale Dataset and Challenge for Earth Surface Forecasting as a Guided Video Prediction Task. - Christian Requena-Mesa; Vitus Benson; Markus Reichstein; Jakob Runge; Joachim Denzler (CVPR Workshops, 2021)

Conditional Adversarial Debiasing: Towards Learning Unbiased Classifiers from Biased Data. - Christian Reimers; Paul Bodesheim; Jakob Runge; Joachim Denzler (GCPR, 2021)

Necessary and sufficient graphical conditions for optimal adjustment sets in causal graphical models with hidden variables. - Jakob Runge (NeurIPS, 2021)

A Data-Driven Approach to Partitioning Net Ecosystem Exchange Using a Deep State Space Model. - Violeta Teodora Trifunov; Maha Shadaydeh; Jakob Runge; Markus Reichstein; Joachim Denzler (IEEE Access, 2021)

Necessary and sufficient conditions for optimal adjustment sets in causal graphical models with hidden variables. - Jakob Runge (CoRR, 2021)

Towards Learning an Unbiased Classifier from Biased Data via Conditional Adversarial Debiasing. - Christian Reimers; Paul Bodesheim; Jakob Runge; Joachim Denzler (CoRR, 2021)

Conditional Independence Testing with Heteroskedastic Data and Applications to Causal Discovery. - Wiebke Günther; Urmi Ninad; Jonas Wahl; Jakob Runge (NeurIPS, 2022)

Vector Causal Inference between Two Groups of Variables. - Jonas Wahl; Urmi Ninad; Jakob Runge (AAAI, 2023)

ClimateSet: A Large-Scale Climate Model Dataset for Machine Learning. - Julia Kaltenborn; Charlotte E. E. Lange; Venkatesh Ramesh; Philippe Brouillard; Yaniv Gurwicz; Chandni Nagda; Jakob Runge; Peer Nowack; David Rolnick (NeurIPS, 2023)

Causal Discovery for time series from multiple datasets with latent contexts. - Wiebke Günther; Urmi Ninad; Jakob Runge (UAI, 2023)

Increasing effect sizes of pairwise conditional independence tests between random vectors. - Tom Hochsprung; Jonas Wahl; Andreas Gerhardus; Urmi Ninad; Jakob Runge (UAI, 2023)

Selecting Robust Features for Machine Learning Applications using Multidata Causal Discovery. - Saranya Ganesh S.; Tom Beucler; Frederick Iat-Hin Tam; Milton S. Gomez; Jakob Runge; Andreas Gerhardus (CoRR, 2023)

Discovering Causal Relations and Equations from Data. - Gustau Camps-Valls; Andreas Gerhardus; Urmi Ninad; Gherardo Varando; Georg Martius; Emili Balaguer-Ballester; Ricardo Vinuesa; Emiliano Diaz; Laure Zanna; Jakob Runge (CoRR, 2023)

A Causal Discovery Approach To Learn How Urban Form Shapes Sustainable Mobility Across Continents. - Felix Wagner 002; Florian Nachtigall; Lukas Franken; Nikola Milojevic-Dupont; Rafael H. M. Pereira; Nicolas Koch; Jakob Runge; Marta Gonzalez; Felix Creutzig (CoRR, 2023)

Projecting infinite time series graphs to finite marginal graphs using number theory. - Andreas Gerhardus; Jonas Wahl; Sofia Faltenbacher; Urmi Ninad; Jakob Runge (CoRR, 2023)

Non-parametric Conditional Independence Testing for Mixed Continuous-Categorical Variables: A Novel Method and Numerical Evaluation. - Oana-Iuliana Popescu; Andreas Gerhardus; Jakob Runge (CoRR, 2023)

Invariance & Causal Representation Learning: Prospects and Limitations. - Simon Bing; Jonas Wahl; Urmi Ninad; Jakob Runge (CoRR, 2023)

Distinguishing Cause and Effect in Bivariate Structural Causal Models: A Systematic Investigation. - Christoph Käding; Jakob Runge (J. Mach. Learn. Res., 2023)

Identifying Linearly-Mixed Causal Representations from Multi-Node Interventions. - Simon Bing; Urmi Ninad; Jonas Wahl; Jakob Runge (CLeaR, 2024)

Bootstrap aggregation and confidence measures to improve time series causal discovery. - Kevin Debeire; Andreas Gerhardus; Jakob Runge; Veronika Eyring (CLeaR, 2024)

Causal discovery with endogenous context variables. - Wiebke Günther; Oana-Iuliana Popescu; Martin Rabel; Urmi Ninad; Andreas Gerhardus; Jakob Runge (NeurIPS, 2024)

A Global Markov Property for Solutions of Stochastic Difference Equations and the corresponding Full Time Graphs. - Tom Hochsprung; Jakob Runge; Andreas Gerhardus (UAI, 2024)

Causal Representation Learning in Temporal Data via Single-Parent Decoding. - Philippe Brouillard; Sébastien Lachapelle; Julia Kaltenborn; Yaniv Gurwicz; Dhanya Sridhar; Alexandre Drouin; Peer Nowack; Jakob Runge; David Rolnick (CoRR, 2024)

Causal Modeling in Multi-Context Systems: Distinguishing Multiple Context-Specific Causal Graphs which Account for Observational Support. - Martin Rabel; Wiebke Günther; Jakob Runge; Andreas Gerhardus (CoRR, 2024)

Invariance & Causal Representation Learning: Prospects and Limitations. - Simon Bing; Tom Hochsprung; Jonas Wahl; Urmi Ninad; Jakob Runge (Trans. Mach. Learn. Res., 2024)

Separation-Based Distance Measures for Causal Graphs. - Jonas Wahl; Jakob Runge (AISTATS, 2025)

Unitless Unrestricted Markov-Consistent SCM Generation: Better Benchmark Datasets for Causal Discovery. - Rebecca Herman; Jonas Wahl; Urmi Ninad; Jakob Runge (CLeaR, 2025)

Non-parametric Conditional Independence Testing for Mixed Continuous-Categorical Variables: A Novel Method and Numerical Evaluation. - Oana-Iuliana Popescu; Andreas Gerhardus; Martin Rabel; Jakob Runge (CLeaR, 2025)

Internal Incoherency Scores for Constraint-based Causal Discovery Algorithms. - Sofia Faltenbacher; Jonas Wahl; Rebecca Herman; Jakob Runge (CoRR, 2025)

Sanity Checking Causal Representation Learning on a Simple Real-World System. - Juan L. Gamella; Simon Bing; Jakob Runge (CoRR, 2025)

Using Time Structure to Estimate Causal Effects. - Tom Hochsprung; Jakob Runge; Andreas Gerhardus (CoRR, 2025)