Causal Inference

Causal inference on process graphs: Causal structure and effect identification

A structural vector autoregressive (SVAR) process is a linear causal model for variables that evolve over a discrete set of time points …

@article{reiterCausalInferenceOnProcess2026,
 author = {Nicolas-Domenic Reiter and Jonas Wahl and Andreas Gerhardus and Jakob Runge},
 doi = {10.3150/25-BEJ1909},
 journal = {Bernoulli},
 month = {5},
 number = {2},
 pages = {1356-1382},
 publisher = {Bernoulli Society for Mathematical Statistics and Probability},
 title = {Causal inference on process graphs: Causal structure and effect identification},
 url = {https://www.dfki.de/fileadmin/user_upload/import/16775_casual_inference_on_process_graphs.pdf},
 volume = {32},
 year = {2026}
}

Separation-Based Distance Measures for Causal Graphs

Assessing the accuracy of the output of causal discovery algorithms is crucial in developing and comparing novel methods. Common …

@inproceedings{wahlSeparationBasedDistanceMeasuresFor2025,
 author = {Jonas Wahl and Jakob Runge},
 booktitle = {Proceedings of The 28th International Conference on Artificial Intelligence and Statistics. International Conference on Artificial Intelligence and Statistics (AISTATS-2025), May 3-5, Mai Khao, Thailand},
 pages = {3412-3420},
 publisher = {Proceedings of Machine Learning Research},
 title = {Separation-Based Distance Measures for Causal Graphs},
 url = {https://proceedings.mlr.press/v258/wahl25b.html},
 volume = {258},
 year = {2025}
}

Unitless Unrestricted Markov-Consistent SCM Generation: Better Benchmark Datasets for Causal Discovery

Causal discovery aims to extract qualitative causal knowledge in the form of causal graphs from data. Because causal ground truth is …

@inproceedings{hermanUnitlessUnrestrictedMarkovConsistentSCM2025,
 author = {Rebecca J Herman and Jonas Wahl and Urmi Ninad and Jakob Runge},
 booktitle = {Proceedings of the Fourth Conference on Causal Learning and Reasoning. Conference on Causal Learning and Reasoning (CLeaR-2025)},
 pages = {1506-1531},
 publisher = {MLResearchPress},
 title = {Unitless Unrestricted Markov-Consistent SCM Generation: Better Benchmark Datasets for Causal Discovery},
 url = {https://proceedings.mlr.press/v275/herman25a.html},
 volume = {275},
 year = {2025}
}