Abstract
Learning temporal causal structures among multiple time series is one of the major tasks in mining time series data. Granger causality is one of the most popular techniques in uncovering the temporal dependencies among time series; however it faces two main challenges: (i) the spurious effect of unobserved time series and (ii) the computational challenges in high dimensional settings. In this paper, we utilize the confounder path delays to find a subset of time series that via conditioning on them we are able to cancel out the spurious confounder effects. After study of consistency of different Granger causality techniques, we propose Copula-Granger and show that while it is consistent in high dimensions, it can efficiently capture non-linearity in the data. Extensive experiments on a synthetic and a social networking dataset confirm our theoretical results.
