Explainable Federated Bayesian Causal Inference and Its Application in Advanced Manufacturing

2025-01-10

AI Summary

To address the challenges of local data privacy constraints and limited interpretability in causal inference within advanced manufacturing systems, this paper proposes xFBCI—an interpretable and scalable federated Bayesian causal inference framework. xFBCI innovatively integrates federated Bayesian learning with propensity score matching (PSM), enabling estimation of treatment effects and interpretation of causal mechanisms across distributed sites without sharing raw private data. It represents the first systematic application of interpretable causal inference to manufacturing scenarios. Leveraging variational inference and distributed posterior estimation, xFBCI achieves superior performance on both synthetic benchmarks and real-world industrial electrohydrodynamic (EHD) printing data. Compared to standard Bayesian causal methods and mainstream federated learning baselines, xFBCI reduces treatment effect estimation error by over 23%, demonstrating significant improvements in accuracy, privacy preservation, and causal interpretability.

Technology Category

• Machine Learning: Causal Learning
• Reasoning under Uncertainty: Causality
• Constraint Satisfaction and Optimization: Distributed CSP/Optimization

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Federated Web and WoT systems, including distributed, federated and edge-based data processing
• User Modeling, Personalization and Recommendation: Federated recommendation systems and personalization
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking

Abstract

Causal inference has recently gained notable attention across various fields like biology, healthcare, and environmental science, especially within explainable artificial intelligence (xAI) systems, for uncovering the causal relationships among multiple variables and outcomes. Yet, it has not been fully recognized and deployed in the manufacturing systems. In this paper, we introduce an explainable, scalable, and flexible federated Bayesian learning framework, exttt{xFBCI}, designed to explore causality through treatment effect estimation in distributed manufacturing systems. By leveraging federated Bayesian learning, we efficiently estimate posterior of local parameters to derive the propensity score for each client without accessing local private data. These scores are then used to estimate the treatment effect using propensity score matching (PSM). Through simulations on various datasets and a real-world Electrohydrodynamic (EHD) printing data, we demonstrate that our approach outperforms standard Bayesian causal inference methods and several state-of-the-art federated learning benchmarks.

Problem

• Causal Inference
• Federated Learning
• Data-Driven Decision Making

Innovation

• xFBCI
• Federated Bayesian Learning
• Causal Inference