[2603.02902] Distributed Dynamic Invariant Causal Prediction in Environmental Time Series

Computer Science > Machine Learning arXiv:2603.02902 (cs) [Submitted on 3 Mar 2026] Title:Distributed Dynamic Invariant Causal Prediction in Environmental Time Series Authors:Ziruo Hao, Tao Yang, Xiaofeng Wu, Bo Hu View a PDF of the paper titled Distributed Dynamic Invariant Causal Prediction in Environmental Time Series, by Ziruo Hao and 3 other authors View PDF HTML (experimental) Abstract:The extraction of invariant causal relationships from time series data with environmental attributes is critical for robust decision-making in domains such as climate science and environmental monitoring. However, existing methods either emphasize dynamic causal analysis without leveraging environmental contexts or focus on static invariant causal inference, leaving a gap in distributed temporal settings. In this paper, we propose Distributed Dynamic Invariant Causal Prediction in Time-series (DisDy-ICPT), a novel framework that learns dynamic causal relationships over time while mitigating spatial confounding variables without requiring data communication. We theoretically prove that DisDy-ICPT recovers stable causal predictors within a bounded number of communication rounds under standard sampling assumptions. Empirical evaluations on synthetic benchmarks and environment-segmented real-world datasets show that DisDy-ICPT achieves superior predictive stability and accuracy compared to baseline methods A and B. Our approach offers promising applications in carbon monitoring and weather...