提出分布式动态因果预测框架，提升环境时间序列的稳定预测能力。
Distributed Dynamic Invariant Causal Prediction in Environmental Time Series
- 基于分布式架构学习时序动态因果关系，避免跨节点数据传输
- 在合成与真实数据上实现更优的预测稳定性和准确性
- 适合碳监测与气象预报等需鲁棒因果建模的场景
从具有环境属性的时间序列中提取不变因果关系，对气候科学和环境监测等领域的稳健决策至关重要。然而，现有方法或侧重动态因果分析而忽略环境背景，或专注静态不变因果推断，缺乏对分布式时序场景的支持。本文提出分布式时序不变因果预测框架 DisDy-ICPT，可在不通信数据的前提下学习随时间演化的因果关系，并缓解空间混杂变量影响。理论上证明，在标准抽样假设下，DisDy-ICPT 在有限轮次内可恢复稳定的因果预测因子。在合成基准与分环境真实数据集上的实证评估显示，其预测稳定性与准确率优于基线方法 A 和 B。该方法在碳监测与天气预报中具广阔应用前景。未来工作将扩展至在线学习场景。
原文摘要 · Abstract (English)
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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