Federated Causal Representation Learning in State-Space Systems for Decentralized Counterfactual Reasoning

TL;DR

This paper introduces a federated causal representation learning framework for state-space systems, enabling decentralized counterfactual reasoning in industrial networks without sharing raw data.

Contribution

It proposes a novel federated approach that learns disentangled latent states for interdependent clients, allowing accurate counterfactual predictions while preserving privacy.

Findings

The method converges to a centralized oracle solution.

It achieves scalable and accurate counterfactual inference.

Experiments validate effectiveness on synthetic and real datasets.

Abstract

Networks of interdependent industrial assets (clients) are tightly coupled through physical processes and control inputs, raising a key question: how would the output of one client change if another client were operated differently? This is difficult to answer because client-specific data are high-dimensional and private, making centralization of raw data infeasible. Each client also maintains proprietary local models that cannot be modified. We propose a federated framework for causal representation learning in state-space systems that captures interdependencies among clients under these constraints. Each client maps high-dimensional observations into low-dimensional latent states that disentangle intrinsic dynamics from control-driven influences. A central server estimates the global state-transition and control structure. This enables decentralized counterfactual reasoning where clients predict how outputs would change under alternative control inputs at others while only exchanging compact latent states. We prove convergence to a centralized oracle and provide privacy guarantees. Our experiments demonstrate scalability, and accurate cross-client counterfactual inference on synthetic and real-world industrial control system datasets.