ICIC_Prediction：基于因果关系全局动态特性的预测方法

因果关系的预测是因果关系研究的重要内容和主要应用。现有的很多预测方法以寻找最优预测方程或最小特征变量集合为目的,以简化计算。提出一种新的可用于处理政策干预的因果关系预测方法ICIC_Prediction,不局限于利用马尔科夫毯等特征变量集合,而是从因果关系网络结构出发,利用因果关系系统及其采样数据的动态全局特性,预测目标变量在当前采样中的取值。通过在NIPS 2008“因果与预测”的评测会议上发布的四个不同类型的数据集上的对比实验,分析并展示了ICIC_Prediction方法的优势和特点。...

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Bibliographic Details
Published in计算机工程与科学 Vol. 37; no. 5; pp. 1001 - 1008
Main Author 李岩 王挺 张晓艳
Format Journal Article
LanguageChinese
Published 国防科学技术大学计算机学院,湖南长沙,410073%国防科学技术大学人文与社会科学学院,湖南长沙,410073 2015
Subjects
因果关系
因果关系分析
因果关系预测
政策干预
概率失效
causality analysis
policy manipulation
因果关系预测
probability failure
政策干预
因果关系
causality prediction
因果关系分析
概率失效
causality
Online AccessGet full text
ISSN1007-130X
DOI10.3969/j.issn.1007-130X.2015.05.022

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Summary:因果关系的预测是因果关系研究的重要内容和主要应用。现有的很多预测方法以寻找最优预测方程或最小特征变量集合为目的,以简化计算。提出一种新的可用于处理政策干预的因果关系预测方法ICIC_Prediction,不局限于利用马尔科夫毯等特征变量集合,而是从因果关系网络结构出发,利用因果关系系统及其采样数据的动态全局特性,预测目标变量在当前采样中的取值。通过在NIPS 2008“因果与预测”的评测会议上发布的四个不同类型的数据集上的对比实验,分析并展示了ICIC_Prediction方法的优势和特点。
Bibliography:Causality prediction is an important part and a promoing application of causality research. To simplify prediction, traditional methods usually use optimal structure equation or minimum feature set, such as Markov Blanket （MB） of the target variable, to make predictions. We present a novel method avoiding some limitations of the traditional methods by using global dynamic properties of cau- sality analysis based on causal structure with samples to make prediction of the target variable under the effect of unknown policy manipulations performed by an external agent. Several experiments have been done to compare GC, VAR and several other popular methods with ICIC_Prediction based on four data- sets published in challenge of “Causation and Prediction”in NIPS 2008 for analyzing and showing the ad- vantages and properties of our method.
43-1258/TP
LI Yan,WANG Ting , ZHANG Xiao-yan （1. College of Computer, National University of Defense Technology, Chang sha 410073; 2. College of Humanities and Social Science, Nation
ISSN:1007-130X
DOI:10.3969/j.issn.1007-130X.2015.05.022