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)

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)

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)

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

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

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

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

SPACETIME: Causal Discovery from Non-Stationary Time Series. - Sarah Mameche; Lénaïg Cornanguer; Urmi Ninad; Jilles Vreeken (AAAI, 2025)

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