Jonas Wahl

Publications & Preprints

Preprints

• S. Faltenbacher*, J. Wahl*, R. Herman, and J. Runge, Internal Incoherency Scores for Constraint-based Causal Discovery Algorithms, Preprint: arXiv:2503.17037. (*equal contribution)
  
  
• R. Herman, J. Wahl, U. Ninad, and J. Runge, Unitless Unrestricted Markov-Consistent SCM Generation: Better Benchmark Datasets for Causal Discovery. Preprint: arXiv:2503.17037. Accepted for Publication at Causal Learning and Reasoning (CLeaR) 2025.
  
  
• P. Brouillard, C. Squires, J. Wahl, K. P. Kording, K. Sachs, A. Drouin, and D. Sridhar, The Landscape of Causal Discovery Data: Grounding Causal Discovery in Real-World Applications. Preprint: arXiv:2412.01953, 2024. Accepted for Publication at Causal Learning and Reasoning (CLeaR) 2025.
  
  
• N.-D. Reiter, J. Wahl, G. C. Hegerl, and J. Runge, Asymptotic Uncertainty in the Estimation of Frequency Domain Causal Effects for Linear Processes. Preprint: arXiv:2406.18191, 2024.
  
  
• N.-D. Reiter, J. Wahl, A. Gerhardus, and J. Runge, Causal Inference on Process Graphs, Part II: Causal Structure and Effect Identification. Preprint: arXiv:2406.17422, 2024.
  
  
• A. Gerhardus, J. Wahl, S. Faltenbacher, U. Ninad, and J. Runge, Projecting infinite time series graphs to finite marginal graphs using number theory. Preprint: arXiv:2310.05526, 2023.
  
  
• N.-D. Reiter, A. Gerhardus, J. Wahl, and J. Runge, Causal Inference on Process Graphs, Part I: The Structural Equation Process Representation. Preprint: arXiv:2305.11561, 2023.
  
  

Publications

• J. Wahl and J. Runge, Separation-based distance measures for causal graphs. Proceedings of The 28th International Conference on Artificial Intelligence and Statistics, PMLR 258:3412-3420, 2025.
  
  
• J. Wahl, U. Ninad, and J. Runge, Foundations of Causal Discovery on Groups of Variables, Journal of Causal Inference, 12 (1), p. 20230041, 2024.
  
  
• S. Bing, T. Hochsprung, J. Wahl, U. Ninad and J. Runge, Invariance & Causal Representation Learning: Prospects and Limitations, Transactions on Machine Learning Research (TMLR), 2835-8856, 2024.
  
  
  
• S. Bing, U. Ninad*, J. Wahl*, and J. Runge, Identifying Linearly-Mixed Causal Representations from Multi-Node Interventions, Proceedings of the Third Conference on Causal Learning and Reasoning (CLeaR), PMLR 236:843-867, 2024.
  
  
• T. Hochsprung, J. Wahl*, A. Gerhardus*, U. Ninad* and J. Runge, Increasing Effect Sizes of Pairwise Conditional Independence Tests between Random Vectors. Proceedings of the Thirty-Ninth Conference on Uncertainty in Artificial Intelligence, PMLR 216:879-889, 2023.
  
  
• J. Wahl*, U. Ninad* and J. Runge, Vector causal inference between two groups of variables.  Proceedings of the AAAI Conference on Artificial Intelligence, 37(10), 12305-12312, 2023. 
  
  
• W. Günther, U. Ninad*, J. Wahl* and J. Runge, Conditional Independence Testing with Heteroskedastic Data and Applications to Causal Discovery, Advances in Neural Information Processing Systems (NeurIPS) 35, 2022.
  
  
• J. Wahl, Traces on Diagram Algebras II: Centralizer algebras of easy groups and new variations of the Young graph. Algebraic Combinatorics 5 (2022), no. 3, 413-436.
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• J. Wahl, Traces On Diagram Algebras I: Free Partition Quantum Groups, Random Lattice Paths And Random Walks On Trees. Journal of the London Mathematical Society 105 (2022), no. 4, 2324-2372.
  
  
• S. Vaes and J. Wahl, Bernoulli actions of type III_1 and L^2-cohomology, Geom. and Func. Anal. (GAFA) 28 (2018), 518–562.
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• Y. Arano, T. de Laat and J. Wahl, The Fourier algebra of a rigid C*-tensor category, Publ. Res. Inst. Math. Sci. 54 (2018), no. 2, 393–410.​
  
  
• P. Tarrago and J. Wahl, Free wreath product quantum groups and standard invariants of subfactors, Advances in Mathematics 331 (2018), 1–57.
  
  
• Y. Arano, T. de Laat and J. Wahl, Howe-Moore type theorems for quantum groups and C*-tensor categories, Compos. Math. 154 (2018), no. 2, 328–341.
  
  
• J. Wahl, A note on reduced and von Neumann algebraic free wreath products, Illinois J. Math. 59 (2015) no. 3, 801–817.
  
  

Workshop Contributions and other Scientific Writings

• C. Lohse and J. Wahl, Sortability of Time Series Data, peer-reviewed contribution to the Workshop Causal Inference for Time Series (CI4TS) at UAI 2024.
  
  
• M. Prasow-Émond, Y. Plancherel, P.J. Mason, M.D. Piggott, and J. Wahl, Impacts of Climate Change on Small Island Nations: A Data Science Framework using Remote Sensing and Observational Time Series, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-18919, 2024.
  
  
• S. Bing, J. Wahl*, U. Ninad* and J. Runge, Invariance & Causal Representation Learning: Prospects and Limitations, peer-reviewed contribution to NeurIPS 2023 Workshop on Causal Representation Learning.
  
  
• M. Langer, K. Baum, K. Hartmann, S. Hessel, T. Speith, J. Wahl, Explainability auditing for intelligent systems: a rationale for multi-disciplinary perspectives. 2021 IEEE 29th International Requirements Engineering Conference Workshops (REW) (2021), 164-168.
  
  
• J. Wahl, Where are all the guns? Modeling firearm ownership in the United States. Patterns 3 (2022), no. 8, 100571.
  This is a review article on a paper using causal inference techniques to model firearm ownership.

PhD Thesis

• Nonsingular Bernoulli actions and quantum symmetries. Arenberg Doctoral School, KU Leuven, 2018.