Building causation links in stochastic nonlinear systems from data
This work addresses the challenge of deciphering causal relationships from data for researchers and practitioners in fields like physics and machine learning, but it appears incremental as it builds on existing theoretical ideas.
The paper tackles the problem of detecting causal links in stochastic nonlinear systems from observational data by developing theoretical ideas from physics and using state-of-the-art machine learning techniques, resulting in the computation of the asymptotic efficiency of a linear response-based causal predictor for a large-scale Markov process network.
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.