Network inference via process motifs for lagged correlation in linear stochastic processes

TL;DR

This paper introduces pairwise edge measures derived from lagged correlation matrices to infer causal networks in linear stochastic processes, offering a fast, accurate alternative to existing methods like Granger causality.

Contribution

The authors propose novel PEMs based on process motifs that correct for confounding and reverse causation, improving network inference from time-series data.

Findings

PEMs outperform or match existing methods in accuracy.

PEMs are computationally faster than traditional approaches.

The approach is effective for slightly autocorrelated data.

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 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.