![[2602.13004] Uncertainty in Federated Granger Causality: From Origins to Systemic Consequences](https://arxiv.org/static/browse/0.3.4/images/arxiv-logo-fb.png){kind=logo}
[2602.13004] Uncertainty in Federated Granger Causality: From Origins to Systemic Consequences
Summary
This paper presents a novel methodology for quantifying uncertainty in Federated Granger Causality, addressing limitations in existing algorithms that overlook uncertainty in causal inference from distributed data.
Why It Matters
Understanding uncertainty in Federated Granger Causality is crucial for enhancing the reliability of causal inference in distributed systems, particularly in applications like smart grids. This research provides a framework that improves interpretability and robustness in decision-making processes reliant on high-dimensional data.
Key Takeaways
- Introduces a methodology for quantifying uncertainty in Federated Granger Causality.
- Differentiates between aleatoric and epistemic sources of uncertainty.
- Derives closed-form recursions for uncertainty propagation in federated systems.
- Establishes convergence conditions that enhance model robustness.
- Demonstrates improved reliability of causal inference through empirical evaluations.
Computer Science > Machine Learning arXiv:2602.13004 (cs) [Submitted on 13 Feb 2026] Title:Uncertainty in Federated Granger Causality: From Origins to Systemic Consequences Authors:Ayush Mohanty, Nazal Mohamed, Nagi Gebraeel View a PDF of the paper titled Uncertainty in Federated Granger Causality: From Origins to Systemic Consequences, by Ayush Mohanty and 2 other authors View PDF Abstract:Granger Causality (GC) provides a rigorous framework for learning causal structures from time-series data. Recent federated variants of GC have targeted distributed infrastructure applications (e.g., smart grids) with distributed clients that generate high-dimensional data bound by data-sovereignty constraints. However, Federated GC algorithms only yield deterministic point estimates of causality and neglect uncertainty. This paper establishes the first methodology for rigorously quantifying uncertainty and its propagation within federated GC frameworks. We systematically classify sources of uncertainty, explicitly differentiating aleatoric (data noise) from epistemic (model variability) effects. We derive closed-form recursions that model the evolution of uncertainty through client-server interactions and identify four novel cross-covariance components that couple data uncertainties with model parameter uncertainties across the federated architecture. We also define rigorous convergence conditions for these uncertainty recursions and obtain explicit steady-state variances for both serv...
