Causal Learning in Biomedical Applications: A Benchmark

Petr Ryšavý, Xiaoyu He, Jakub Mareček
6/21/2024
cs.LG

No official code found. Best implementation: petrrysavy/krebsdynotears (0 stars, MIT, last commit 2025-09-08).

Abstract

Learning causal relationships between a set of variables is a challenging problem in computer science. Many existing artificial benchmark datasets are based on sampling from causal models and thus contain residual information that the ${R} ^2$-sortability can identify. Here, we present a benchmark for methods in causal learning using time series. The presented dataset is not ${R}^2$-sortable and is based on a real-world scenario of the Krebs cycle that is used in cells to release energy. We provide four scenarios of learning, including short and long time series, and provide guidance so that testing is unified between possible users.

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petrrysavy/krebsdynotearsCommunity100%
MIT
00PythonJun 5, 20241 years agoMIT

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Cite this paper

@article{ryav2024causal,
  title  = {Causal Learning in Biomedical Applications: A Benchmark},
  author = {Petr Ryšavý and Xiaoyu He and Jakub Mareček},
  year   = {2024},
  eprint = {2406.15189},
  archivePrefix = {arXiv},
  url    = {http://arxiv.org/abs/2406.15189v2}
}

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