Work

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

Year: 2026

Type: article

Abstract: 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.

Source: Mathematics

DOI: 10.3390/math14071129

Authors: Minglong Gao, Yingchun Zhou

Institution: East China Normal University

Language: English

Topic: Bayesian Modeling and Causal Inference

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

Open Access status: gold

Estimated APC paid: $3,137

Funder: Natural Science Foundation of Shanghai