Variable-lag Granger Causality for Time Series Analysis
This addresses a key limitation in causal inference for time series analysis across domains like social and biological sciences, offering a more flexible approach.
The paper tackles the problem of Granger causality assuming fixed time delays, which fails in many real-world applications like collective behavior and finance, by developing variable-lag Granger causality that allows arbitrary delays and shows better performance than existing methods in simulated and real-world datasets.
Granger causality is a fundamental technique for causal inference in time series data, commonly used in the social and biological sciences. Typical operationalizations of Granger causality make a strong assumption that every time point of the effect time series is influenced by a combination of other time series with a fixed time delay. However, the assumption of the fixed time delay does not hold in many applications, such as collective behavior, financial markets, and many natural phenomena. To address this issue, we develop variable-lag Granger causality, a generalization of Granger causality that relaxes the assumption of the fixed time delay and allows causes to influence effects with arbitrary time delays. In addition, we propose a method for inferring variable-lag Granger causality relations. We demonstrate our approach on an application for studying coordinated collective behavior and show that it performs better than several existing methods in both simulated and real-world datasets. Our approach can be applied in any domain of time series analysis.