Saved in:
| Main Authors: | , , , , |
|---|---|
| Format: | Preprint |
| Published: |
2025
|
| Subjects: | |
| Online Access: | https://arxiv.org/abs/2509.07701 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866916942850293760 |
|---|---|
| author | Chibbaro, Sergio Furtlehner, Cyril Marchetta, Théo Pantea, Andrei-Tiberiu Rossetti, Davide |
| author_facet | Chibbaro, Sergio Furtlehner, Cyril Marchetta, Théo Pantea, Andrei-Tiberiu Rossetti, Davide |
| contents | Causal relationships play a fundamental role in understanding the world around us. The ability to identify and understand cause-effect relationships is critical to making informed decisions, predicting outcomes, and developing effective strategies. However, deciphering causal relationships from observational data is a difficult task, as correlations alone may not provide definitive evidence of causality. In recent years, the field of machine learning (ML) has emerged as a powerful tool, offering new opportunities for uncovering hidden causal mechanisms and better understanding complex systems. In this work, we address the issue of detecting the intrinsic causal links of a large class of complex systems in the framework of the response theory in physics. We develop some theoretical ideas put forward by [1], and technically we use state-of-the-art ML techniques to build up models from data. We consider both linear stochastic and non-linear systems. Finally, we compute the asymptotic efficiency of the linear response based causal predictor in a case of large scale Markov process network of linear interactions. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2509_07701 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Building causation links in stochastic nonlinear systems from data Chibbaro, Sergio Furtlehner, Cyril Marchetta, Théo Pantea, Andrei-Tiberiu Rossetti, Davide Statistical Mechanics Machine Learning Causal relationships play a fundamental role in understanding the world around us. The ability to identify and understand cause-effect relationships is critical to making informed decisions, predicting outcomes, and developing effective strategies. However, deciphering causal relationships from observational data is a difficult task, as correlations alone may not provide definitive evidence of causality. In recent years, the field of machine learning (ML) has emerged as a powerful tool, offering new opportunities for uncovering hidden causal mechanisms and better understanding complex systems. In this work, we address the issue of detecting the intrinsic causal links of a large class of complex systems in the framework of the response theory in physics. We develop some theoretical ideas put forward by [1], and technically we use state-of-the-art ML techniques to build up models from data. We consider both linear stochastic and non-linear systems. Finally, we compute the asymptotic efficiency of the linear response based causal predictor in a case of large scale Markov process network of linear interactions. |
| title | Building causation links in stochastic nonlinear systems from data |
| topic | Statistical Mechanics Machine Learning |
| url | https://arxiv.org/abs/2509.07701 |