Transfer entropy from non-uniform embedding is a popular tool for the inference of causal relationships among dynamical subsystems. In this study we present an approach that makes use of low-dimensional conditional mutual information quantities to decompose the original high-dimensional conditional mutual information in the searching procedure of non-uniform embedding for significant variables at different lags. We perform a series of simulation experiments to assess the sensitivity and specificity of our proposed method to demonstrate its advantage compared to previous algorithms. The results provide concrete evidence that low-dimensional approximations can help to improve the statistical accuracy of transfer entropy in multivariate causality analysis and yield a better performance over other methods. The proposed method is especially efficient as the data length grows.

非均匀嵌入的传递熵是一种用于推断动力学子系统之间因果关系的流行工具。在这项研究中，我们提出了一种方法，该方法利用低维条件互信息量在不均匀嵌入的搜索过程中分解不同延迟的原始高维条件互信息。我们进行了一系列的仿真实验，以评估我们提出的方法的敏感性和特异性，以证明其与以前算法相比的优势。结果提供了具体的证据，即低维近似可以帮助提高多元因果关系分析中转移熵的统计准确性，并且比其他方法具有更好的性能。