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

Abstract

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

CLC Number:

TP391

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.

Figures/Tables 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

Overview of observational data-based time series causal inference

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