Building causation links in stochastic nonlinear systems from data

• URL: http://arxiv.org/abs/2509.07701v1
• Date: Tue, 09 Sep 2025 13:07:29 GMT
• Title: Building causation links in stochastic nonlinear systems from data
• Authors: Sergio Chibbaro, Cyril Furtlehner, Théo Marchetta, Andrei-Tiberiu Pantea, Davide Rossetti,
• Abstract summary: Causal relationships play a fundamental role in understanding the world around us.<n>The ability to identify and understand cause-effect relationships is critical to making informed decisions, predicting outcomes, and developing effective strategies.<n>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.
• Score: 3.1053323925902956
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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