Causal Learning in Biomedical Applications: Krebs Cycle as a Benchmark

TL;DR

This paper introduces a new, realistic benchmark dataset based on the Krebs cycle for evaluating causal discovery methods on time series data, addressing limitations of synthetic datasets.

Contribution

It provides a reproducible, interpretable, and complex benchmark dataset with ground-truth causal graphs, supporting diverse evaluation metrics and multiple causal discovery methods.

Findings

14 causal discovery methods evaluated

Performance varies across different scenarios

Benchmark facilitates fair comparison and development

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 non-trivial benchmark for testing causal learning methods on time series data. It avoids common pitfalls such as residual structural patterns and supports interventions and evaluation with known causal ground truth. This makes it a useful tool for the development and comparison of causal discovery algorithms.