Exploring Causal Relationships with Streaming Features

Kui Yu, Xindong Wu, Wei Ding, Hao Wang

Abstract: Causal discovery is highly desirable in science and technology. In this paper, we study a new research problem of discovery of causal relationships in the context of streaming features, where the features steam in one by one. With a Bayesian network to represent causal relationships, we propose a novel algorithm called causal discovery from streaming features (CDFSF) which consists of a two-phase scheme. In the first phase, CDFSF dynamically discovers causal relationships between each feature seen so far with an arriving feature, while in the second phase CDFSF removes the false positives of each arrived feature from its current set of direct causes and effects. To improve the efficiency of CDFSF, using the symmetry properties between parents (causes) and children (effects) in a faithful Bayesian network, we present a variant of CDFSF, S-CDFSF. Experimental results validate our algorithms in comparison with the existing algorithms of causal relationship discovery.