推论
传递熵
因果推理
计算机科学
因果关系
熵(时间箭头)
数学
算法
最大熵原理
理论计算机科学
人工智能
计量经济学
政治学
量子力学
物理
法学
作者
Jie Sun,Dane Taylor,Erik M. Bollt
出处
期刊:Siam Journal on Applied Dynamical Systems
[Society for Industrial and Applied Mathematics]
日期:2015-01-01
卷期号:14 (1): 73-106
被引量:142
摘要
The broad abundance of time series data, which is in sharp contrast to limited knowledge of the underlying network dynamic processes that produce such observations, calls for a rigorous and efficient method of causal network inference. Here we develop mathematical theory of causation entropy, an information-theoretic statistic designed for model-free causality inference. For stationary Markov processes, we prove that for a given node in the network, its causal parents forms the minimal set of nodes that maximizes causation entropy, a result we refer to as the optimal causation entropy principle. Furthermore, this principle guides us to develop computational and data efficient algorithms for causal network inference based on a two-step discovery and removal algorithm for time series data for a network-couple dynamical system. Validation in terms of analytical and numerical results for Gaussian processes on large random networks highlight that inference by our algorithm outperforms previous leading methods including conditioned Granger causality and transfer entropy. Interestingly, our numerical results suggest that the number of samples required for accurate inference depends strongly on network characteristics such as the density of links and information diffusion rate and not necessarily on the number of nodes.
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