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.
翻译:时间序列数据因果推算的主要挑战是计算可行性和准确性之间的权衡。我们建议通过对称边缘测量(PEM)来推断因果关系网络,这种测量可以很容易地从滞后的关联基体中计算出来。我们受时间序列数据因时间序列数据造成的因果推算因素和误差的牵动,我们制定了两部PEM,用于弥补各种因素和反向因果关系。为了展示我们的PEM的性能,我们考虑了线性分析过程模拟的网络干扰,我们发现我们提议的PEMs可以准确和有效地推断网络。具体地说,对于略为自动的时序数据,我们的方法达到比Granger因果性高或相近于或相近于的偏差、转移酶和相近的交叉映射,但我们快速和准确的计算时间比任何这些方法都短得多。我们快速和准确的PEMs是执行网络从线性分析过程的模拟中得到的网络干扰,我们展示的网络性方法可以准确和高效地推断网络的网络性,包括清晰的理论基础,它们提供了从直方位模型到精确度模型的准确度模型的准确度估算。