Network inference via process motifs for lagged correlation in linear stochastic processes
This work addresses the problem of efficient network inference for researchers analyzing linear stochastic processes, offering a fast and accurate alternative to current methods, though it appears incremental as it builds on existing process motifs.
The authors tackled the challenge of balancing computational feasibility and accuracy in causal inference from time-series data by proposing pairwise edge measures (PEMs) based on lagged correlation matrices, which achieve higher or similar accuracies to existing methods like Granger causality but with much shorter computation time.
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.