mlsanityData to 14 Oct 2026

Distributed Dynamic Invariant Causal Prediction in Environmental Time Series

arXiv 2603.02902 ↗First posted 3 Mar 2026cs.LGcited by 0

Not on the map. Why?

Abstract (arXiv)

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.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 19 of its 30 references that are in the map. First posted on arXiv 3 Mar 2026.

Builds on

19 of 30 references.

References · 19

  1. 2016Communication-Efficient Learning of Deep Networks from Decentralized Datacited by 7,669
  2. 2019Advances and Open Problems in Federated Learningcited by 3,046
  3. 2018Federated Optimization in Heterogeneous Networkscited by 2,987
  4. 2019Invariant Risk Minimizationcited by 1,854
  5. 2015Causal inference using invariant prediction: identification and confidence intervalscited by 536
  6. 2017Detecting causal associations in large nonlinear time series datasetscited by 244
  7. 2019Causal Discovery from Heterogeneous/Nonstationary Data with Independent Changescited by 166
  8. 2020Amortized Causal Discovery: Learning to Infer Causal Graphs from Time-Series Datacited by 100
  9. 2017Invariant Causal Prediction for Sequential Datacited by 63
  10. 2024Federated Causal Discovery from Heterogeneous Datacited by 15
  11. 2020A review of spatial causal inference methods for environmental and epidemiological applicationscited by 12
  12. 2021Causal Discovery from Conditionally Stationary Time Seriescited by 12
7 more
  1. 2023Federated Causality Learning with Explainable Adaptive Optimizationcited by 11
  2. 2023CDANs: Temporal Causal Discovery from Autocorrelated and Non-Stationary Time Series Datacited by 7
  3. 2022Time Series Causal Link Estimation under Hidden Confounding using Knockoff Interventionscited by 5
  4. 2022Differentiable Invariant Causal Discoverycited by 3
  5. 2022Granger Causality using Neural Networkscited by 2
  6. 2022Federated Causal Discovery From Interventionscited by 1
  7. 2025Federated Causal Inference in Healthcare: Methods, Challenges, and Applicationscited by 1

Built on by

Not cited yet.