Causal Learning in Biomedical Applications: A Benchmark
This work addresses the need for standardized evaluation in causal learning for biomedical applications, but it is incremental as it builds on existing benchmark concepts.
The authors tackled the challenge of evaluating causal learning methods by creating a benchmark dataset based on the real-world Krebs cycle, which is not R²-sortable, and provided four scenarios with short and long time series to unify testing.
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.