因果信息在不同粒度上的迁移性

doi:10.11896/j.issn.1002-137X.2019.02.028

计算机科学 ›› 2019, Vol. 46 ›› Issue (2): 178-186.doi: 10.11896/j.issn.1002-137X.2019.02.028

因果信息在不同粒度上的迁移性

姚宁^1,2, 苗夺谦^1,2, 张志飞^1,3

同济大学计算机科学与技术系上海201804¹
同济大学嵌入式系统和服务计算教育部重点实验室上海201804²
南京大学计算机软件新技术国家重点实验室南京210023³

收稿日期:2018-02-20 出版日期:2019-02-25 发布日期:2019-02-25
通讯作者: 苗夺谦(1964-),男,博士,教授,CCF杰出会员,主要研究方向为粒度计算、机器学习、大数据分析,E-mail:dqmiao@tongji.edu.cn
作者简介:姚宁(1985-),女,博士生,CCF学生会员,主要研究方向为粗糙集、不确定性推理、数据分析,E-mail:ningyao.cn@gmail.com;张志飞(1986-),男,博士,讲师,CCF会员,主要研究方向为机器学习、自然语言处理。
基金资助:
本文受国家重点研发计划(213),国家自然科学基金项目(61673301,61573255,61573259,61673299),公安部重大专项(20170004),南京大学计算机软件新技术国家重点实验室开放课题基金项目(KFKT2017B22)资助。

Transportability of Causal Information Across Different Granularities

YAO Ning^1,2, MIAO Duo-qian^1,2, ZHANG Zhi-fei^1,3

Department of Computer Science and Technology,Tongji University,Shanghai 201804,China¹
Key Laboratory of Embedded System & Service Computing,Ministry of Education of China,Tongji University,Shanghai 201804,China²
State Key Laboratory for Novel Software Technology,Nanjing University,Nanjing 210023,China³

Received:2018-02-20 Online:2019-02-25 Published:2019-02-25

摘要/Abstract

摘要： 知识与粒度相关,在不同粒度上对现象的解释不同,而因果性描述的是现象的本质特征。因果性与粒度之间存在着怎样的关联,一个粒度上的因果关系是否可移植到其他不同粒度上,是目前人工智能研究亟待解决的问题。针对由观测数据构成的信息系统,从数据中直接抽取因果变量所需满足的基本图形结构,估算变量间的因果关系;再通过向系统中添加新属性以及合并多个信息系统,改变原系统中信息的粒度,研究所识别的因果关系在新系统中的可迁移性。若新属性作用于结果变量,则原系统中的因果关系不可迁移至新系统;若新属性对结果变量无影响,则原系统中的因果关系可移植至新系统。

关键词: 粗糙集, 干预, 可迁移性, 粒度, 因果关系, 因果图

Abstract: The knowledge we learned is grain-dependent,which leads to different explanations for a phenomena at different granularities.Causality characterizes the essence of the phenomena.These factors raise an urgent problem currently to be solved in artificial intelligence:the relationship between causality and granularity as well as the transportability of causal effect at one granularity over to a different granularity.Aiming at the information system gathered from observational data,the basic graphical structures required for causal variables can be extracted directly from the data.According to these structures,the causal effects between variables can be computed.By adding new attributes to system and merging multiple information systems,the granularity in the original system is changed and then the issue of whe-ther the causal effect can be transported to the new system is settled in detail.The causal relationship from the original system cannot be transported to the new system if the new attribute acts on the effect variable,otherwise the transporta-bility is feasible in the new system.

Key words: Causal diagram, Causal relationship, Granularity, Interventions, Rough set, Transportability

中图分类号:

TP18

姚宁, 苗夺谦, 张志飞. 因果信息在不同粒度上的迁移性[J]. 计算机科学, 2019, 46(2): 178-186. https://doi.org/10.11896/j.issn.1002-137X.2019.02.028

YAO Ning, MIAO Duo-qian, ZHANG Zhi-fei. Transportability of Causal Information Across Different Granularities[J]. Computer Science, 2019, 46(2): 178-186. https://doi.org/10.11896/j.issn.1002-137X.2019.02.028

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因果信息在不同粒度上的迁移性

Transportability of Causal Information Across Different Granularities

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