  A major challenge for causal inference from time-series data is the trade-off
between computational feasibility and accuracy. Motivated by process motifs for
lagged covariance in an autoregressive model with slow mean-reversion, we
propose to infer networks of causal relations via pairwise edge measure (PEMs)
that one can easily compute from lagged correlation matrices. Motivated by
contributions of process motifs to covariance and lagged variance, we formulate
two PEMs that correct for confounding factors and for reverse causation. To
demonstrate the performance of our PEMs, we consider network interference from
simulations of linear stochastic processes, and we show that our proposed PEMs
can infer networks accurately and efficiently. Specifically, for slightly
autocorrelated time-series data, our approach achieves accuracies higher than
or similar to Granger causality, transfer entropy, and convergent crossmapping
-- but with much shorter computation time than possible with any of these
methods. Our fast and accurate PEMs are easy-to-implement methods for network
inference with a clear theoretical underpinning. They provide promising
alternatives to current paradigms for the inference of linear models from
time-series data, including Granger causality, vector-autoregression, and
sparse inverse covariance estimation.
