因果信息在不同粒度上的迁移性

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。

Transportability of Causal Information Across Different Granularities

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.