Distributed Dynamic Invariant Causal Prediction in Environmental Time Series

TL;DR

Empirical evaluations show that DisDy-ICPT achieves superior predictive stability and accuracy compared to baseline methods A and B and will extend DisDy-ICPT to online learning scenarios and offers promising applications in carbon monitoring and weather forecasting.

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.

Figures (1)

• Figure 1: The framework of DisDy-ICPT