A comprehensive understanding of causality is critical for navigating and operating within today's complex real-world systems. The absence of realistic causal models with known data generating processes complicates fair benchmarking. In this paper, we present the CausalMan simulator, modeled after a real-world production line. The simulator features a diverse range of linear and non-linear mechanisms and challenging-to-predict behaviors, such as discrete mode changes. We demonstrate the inadequacy of many state-of-the-art approaches and analyze the significant differences in their performance and tractability, both in terms of runtime and memory complexity. As a contribution, we will release the CausalMan large-scale simulator. We present two derived datasets, and perform an extensive evaluation of both.
View on arXiv@article{tagliapietra2025_2502.12707,
title={ CausalMan: A physics-based simulator for large-scale causality },
author={ Nicholas Tagliapietra and Juergen Luettin and Lavdim Halilaj and Moritz Willig and Tim Pychynski and Kristian Kersting },
journal={arXiv preprint arXiv:2502.12707},
year={ 2025 }
}