基于观测数据的时间序列因果推断综述

doi:10.11959/j.issn.2096-0271.2022059

摘要/Abstract

摘要：

数据存储量的扩大和计算能力的提升为基于观测数据推断时间序列的因果关系开辟了新途径。在时间序列因果推断的基本性质和研究现状的基础上，系统梳理了5种基于观测数据的时间序列因果推断方法，即Granger因果分析方法、基于信息论的方法、因果网络结构学习算法、基于结构因果模型的方法和基于非线性状态空间模型的方法。然后，根据不同应用场景的数据特点，结合方法的功能和适配性，对基于观测数据的时间序列因果推断方法在经济金融、医疗和生物学、地球系统科学和其他工程领域的典型应用进行了简要介绍。最后，结合时间序列因果推断的重难点问题，比较5种方法的优缺点，分析下一步研究重点，展望未来的研究方向。

关键词: 时间序列, 因果推断, Granger因果分析, 信息熵, 贝叶斯网络, 结构因果模型, 非线性状态空间模型

Abstract:

With the increase of data storage and the improvement of computing power,using observational data to infer time series causality has become a novel approach.Based on the properties and research status of time series causal inference,five observational data-based methods were induced,including Granger causal analysis,information theory-based method,causal network structure learning algorithm,structural causal model-based method and method based on nonlinear state-space model.Then we briefly introduced typical applications in economics and finance,medical science and biology,earth system science and other engineering fields.Further,we compared the advantages and disadvantages and analyzed the ways for improvement of the five methods according to the focus and difficulties of time series causal inference.Finally,we looked into the future research directions.

Key words: time series, causal inference, Granger causal analysis, information entropy, Bayesian network, structural causal model, nonlinear state space model

中图分类号:

TP391

曾泽凡, 陈思雅, 龙洗, 金光. 基于观测数据的时间序列因果推断综述[J]. 大数据, 2023, 9(4): 139-158.

Zefan ZENG, Siya CHEN, Xi LONG, Guang JIN. Overview of observational data-based time series causal inference[J]. Big Data Research, 2023, 9(4): 139-158.

图/表 2

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方法名称	模型/指标	推断标准
Granger因果分析方法	向量自回归模型	回归模型的预测误差
基于信息论的方法	信息熵	变量的不确定度（概率密度）
因果网络结构学习算法	因果贝叶斯网络	d-分离原则、条件独立性、评价函数等
基于结构因果模型的方法	结构因果模型	残差与变量序列的独立性
基于非线性状态空间模型的方法	非线性状态空间模型	重构系统的动力学特性