| 2025 | AISTATS | Separation-Based Distance Measures for Causal Graphs. | Jonas Wahl, Jakob Runge |
| 2025 | ICML | Sanity Checking Causal Representation Learning on a Simple Real-World System. | Juan L. Gamella, Simon Bing, Jakob Runge |
| 2024 | UAI | A Global Markov Property for Solutions of Stochastic Difference Equations and the corresponding Full Time Graphs. | Tom Hochsprung, Jakob Runge, Andreas Gerhardus |
| 2023 | AAAI | Vector Causal Inference between Two Groups of Variables. | Jonas Wahl, Urmi Ninad, Jakob Runge |
| 2023 | UAI | Causal Discovery for time series from multiple datasets with latent contexts. | Wiebke Gnther, Urmi Ninad, Jakob Runge |
| 2023 | UAI | Increasing effect sizes of pairwise conditional independence tests between random vectors. | Tom Hochsprung, Jonas Wahl, Andreas Gerhardus, Urmi Ninad, Jakob Runge |
| 2021 | CVPR | 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 |
| 2021 | CVPR | 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 |
| 2020 | ECCV | Determining the Relevance of Features for Deep Neural Networks. | Christian Reimers, Jakob Runge, Joachim Denzler |
| 2020 | UAI | Discovering contemporaneous and lagged causal relations in autocorrelated nonlinear time series datasets. | Jakob Runge |
| 2018 | AISTATS | Conditional independence testing based on a nearest-neighbor estimator of conditional mutual information. | Jakob Runge |