{"ID":2873030,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2509.07701","arxiv_id":"2509.07701","title":"Building causation links in stochastic nonlinear systems from data","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.","short_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...","url_abs":"https://arxiv.org/abs/2509.07701","url_pdf":"https://arxiv.org/pdf/2509.07701v1","authors":"[\"Sergio Chibbaro\",\"Cyril Furtlehner\",\"Théo Marchetta\",\"Andrei-Tiberiu Pantea\",\"Davide Rossetti\"]","published":"2025-09-09T13:07:29Z","proceeding":"cond-mat.stat-mech","tasks":"[\"cond-mat.stat-mech\",\"cs.LG\"]","methods":"[]","has_code":false}