Paper 3

Enhanced Fast Causal Network Inference over Event Streams

Authors: Saurav Acharya and Byung Suk Lee

Volume 17 (2015)

Abstract

This paper addresses causal inference and modeling over event streams where data have high throughput, are unbounded, and may arrive out of order. The availability of large amount of data with these characteristics presents several new challenges related to causal modeling, such as the need for fast causal inference operations while en- suring consistent and valid results. There is no existing work specically for such a streaming environment. We meet the challenges by introduc- ing a time-centric causal inference strategy which leverages temporal precedence information to decrease the number of conditional indepen- dence tests required to establish the causalities between variables in a causal network. (Dependency and temporal precedence of cause over ef- fect are the two properties of a causal relationship.) Moreover, we employ change-driven causal network inference to safely reduce the running time further. In this paper we present the Order-Aware Temporal Network In- ference algorithm to model the temporal precedence relationships into a temporal network and then propose the Enhanced Fast Causal Network Inference algorithm for learning a causal network faster using the tempo- ral network. Experiments using synthetic and real datasets demonstrate the ecacy of the proposed algorithms.