Distributed Dynamic Invariant Causal Prediction in Environmental Time Series

TL;DR

DisDy-ICPT is a novel distributed framework that learns dynamic causal relationships in environmental time series data, improving stability and accuracy without data sharing, with applications in climate science.

Contribution

The paper introduces DisDy-ICPT, a new method for distributed dynamic causal inference in environmental time series, addressing spatial confounding and communication constraints.

Findings

DisDy-ICPT recovers stable causal predictors within limited communication rounds.

It outperforms baseline methods in predictive stability and accuracy.

Demonstrates effectiveness on synthetic and real-world environmental datasets.

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.