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

Characterization of causal ancestral graphs for time series with latent confounders. - Andreas Gerhardus (CoRR, 2021)

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)

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)

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

Leveraging Causal Information for Multivariate Timeseries Anomaly Detection. - Lukas Heppel; Andreas Gerhardus; Ferdinand Rewicki; Jan Christian Deeken; Günther Waxenegger-Wilfing (DX, 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 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)

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)

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