CausalMan: A physics-based simulator for large-scale causality
Fuente:
arXiv
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| Autori principali: | , , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2025
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| Soggetti: | |
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| _version_ | 1866912236035899392 |
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| author | Tagliapietra, Nicholas Luettin, Juergen Halilaj, Lavdim Willig, Moritz Pychynski, Tim Kersting, Kristian |
| author_facet | Tagliapietra, Nicholas Luettin, Juergen Halilaj, Lavdim Willig, Moritz Pychynski, Tim Kersting, Kristian |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2502_12707 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | CausalMan: A physics-based simulator for large-scale causality Tagliapietra, Nicholas Luettin, Juergen Halilaj, Lavdim Willig, Moritz Pychynski, Tim Kersting, Kristian Machine Learning 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. |
| title | CausalMan: A physics-based simulator for large-scale causality |
| topic | Machine Learning |
| url | https://arxiv.org/abs/2502.12707 |