[2406.15189] Causal Learning in Biomedical Applications: Krebs Cycle as a Benchmark

Computer Science > Machine Learning arXiv:2406.15189 (cs) [Submitted on 21 Jun 2024 (v1), last revised 20 Mar 2026 (this version, v3)] Title:Causal Learning in Biomedical Applications: Krebs Cycle as a Benchmark Authors:Xiaoyu He, Petr Ryšavý, Jakub Mareček View a PDF of the paper titled Causal Learning in Biomedical Applications: Krebs Cycle as a Benchmark, by Xiaoyu He and Petr Ry\v{s}av\'y and Jakub Mare\v{c}ek View PDF HTML (experimental) Abstract:Learning causal relationships from time series data is an important but challenging problem. Existing synthetic datasets often contain hidden artifacts that can be exploited by causal discovery methods, reducing their usefulness for benchmarking. We present a new benchmark dataset based on simulations of the Krebs cycle, a key biochemical pathway. The data are generated using a particle-based simulator that models molecular interactions in a controlled environment. Four distinct scenarios are provided, varying in time series length, number of samples, and intervention settings. The benchmark includes ground-truth causal graphs for evaluation. It supports quantitative comparisons using metrics such as Structural Hamming Distance, Structural Intervention Distance, and F1-score. A comprehensive evaluation of 14 causal discovery methods from different modelling paradigms is presented. Performance is compared across datasets using multiple accuracy and efficiency measures. The dataset provides a reproducible, interpretable, and no...