Enhanced Causal Discovery for Autocorrelated Time Series via Adaptive Momentary Conditional Independence

Discovering causal relationships from time series data is essential for understanding complex dynamical systems across a range of domains. However, strong autocorrelation often limits the detection power of existing algorithms and increases the risk of false positives. To address these challenges, the Adaptive Momentary Conditional Independence (aMCI) method is introduced to mitigate the masking effects of autocorrelation and maintain control over false discovery rates. The aMCI method adaptively modifies the conditioning set to reduce the impact of autocorrelation on the accuracy of causal discovery. In addition, a multi-phase algorithm, the Enhanced Causal Discovery via aMCI (ECD-aMCI) algorithm, is proposed to robustly learn the causal graph by effectively applying the aMCI framework. The algorithm is designed to be hyperparameter-insensitive, order-independent, and provably consistent under oracle conditions. Extensive evaluations on simulated and benchmark datasets demonstrate that the proposed algorithm substantially improves the accuracy of causal discovery from time series, especially in the presence of strong autocorrelation.