Non-Asymptotic Guarantees for Reliable Identification of Granger Causality via the LASSO
基于 LASSO 可靠识别格兰杰因果关系的非渐近保证
Granger causality is among the widely used data-driven approaches for causal analysis of time series data with applications in various areas including economics, molecular biology, and neuroscience. Two of the main challenges of this methodology are: 1) over-fitting as a result of limited data duration, and 2) correlated process noise as a confounding factor, both leading to errors in identifying the causal influences. Sparse estimation via the LASSO has successfully…