Causal Discovery

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}
}

The Landscape of Causal Discovery Data: Grounding Causal Discovery in Real-World Applications

Causal discovery aims to automatically uncover causal relationships from data, a capability with significant potential across many …

@inproceedings{brouillardTheLandscapeOfCausal2025,
 author = {Philippe Brouillard and Chandler Squires and Jonas Wahl and Konrad Körding and Karen Sachs and Alexandre Drouin and Dhanya Sridhar},
 booktitle = {Proceedings of the Fourth Conference on Causal Learning and Reasoning. Conference on Causal Learning and Reasoning (CLeaR-2025)},
 organization = {PMLR},
 pages = {834-873},
 publisher = {MLResearchPress},
 title = {The Landscape of Causal Discovery Data: Grounding Causal Discovery in Real-World Applications},
 url = {https://proceedings.mlr.press/v275/brouillard25a},
 volume = {275},
 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}
}