🤖 AI Summary
To address the trade-off between computational efficiency and estimation accuracy in time-series causal inference, this paper proposes Pairwise Edge Measures (PEMs) grounded in process motifs—introducing process motif theory to lagged covariance modeling for the first time. The proposed PEMs comprise two variants, each analytically correcting for confounding and reverse causation, respectively. The method provides rigorous theoretical guarantees for linear stochastic processes and admits a lightweight implementation requiring only the lagged correlation matrix. In extensive simulations, PEMs match or exceed the accuracy of Granger causality, transfer entropy, and convergent cross-mapping, while reducing computational time substantially—particularly advantageous for large-scale linear systems.
📝 Abstract
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 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.
