Distributed Dynamic Invariant Causal Prediction in Environmental Time Series

• URL: http://arxiv.org/abs/2603.02902v1
• Date: Tue, 03 Mar 2026 11:52:35 GMT
• Title: Distributed Dynamic Invariant Causal Prediction in Environmental Time Series
• Authors: Ziruo Hao, Tao Yang, Xiaofeng Wu, Bo Hu,
• Abstract summary: We propose Distributed Dynamic Invariant Causal Prediction in Time-series (DisDy-ICPT)<n>DisDy-ICPT learns dynamic causal relationships over time while mitigating spatial confounding variables without requiring data communication.<n> 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.
• Score: 9.779350203438485
• License: http://creativecommons.org/publicdomain/zero/1.0/
• 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 forecasting. Future work will extend DisDy-ICPT to online learning scenarios.

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