Review on Causality Detection Based on Empirical Dynamic Modeling

Abstract

In contemporary scientific research, understanding the distinction between correlation and causation is crucial. While correlation is a widely used analytical standard, it does not inherently imply causation. This paper addresses the potential for misinterpretation in relying solely on correlation, especially in the context of nonlinear dynamics. Despite the rapid development of various correlation research methodologies, including machine learning, the exploration into mining causal correlations between variables remains ongoing. Empirical Dynamic Modeling (EDM) emerges as a data-driven framework for modeling dynamic systems, distinguishing itself by eschewing traditional formulaic methods in data analysis. Instead, it reconstructs dynamic system behavior directly from time series data. The fundamental premise of EDM is that dynamic systems can be conceptualized as processes where a set of states, governed by specific rules, evolve over time in a high-dimensional space. By reconstructing these evolving states, dynamic systems can be effectively modeled. Using EDM, this paper explores the detection of causal relationships between variables within dynamic systems through their time series data. It posits that if variable X causes variable Y, then the information about X is inherent in Y and can be extracted from Y's data. This study begins by examining the dialectical relationship between correlation and causation, emphasizing that correlation does not equate to causation, and the absence of correlation does not necessarily indicate a lack of causation.