Surjective Independence of Causal Influences for Local Bayesian Network Structures

2025-09-29

AI Summary

In Bayesian network modeling, the conventional Causal Influence Independence (ICI) assumption is overly restrictive, imposing excessive cognitive burden on domain experts and hindering model scalability. To address this, we propose Surjective Causal Influence Independence (SICI)—a weaker, empirically verifiable local independence assumption that substantially relaxes ICI while significantly reducing the number of expert judgments required. Leveraging SICI, we develop an interpretable, locally structured parameterization framework that synergistically integrates expert knowledge with data-driven learning, ensuring semantic transparency while achieving substantial parameter compression. Experimental results demonstrate that our approach maintains inference accuracy while reducing the number of parameters by one to two orders of magnitude, thereby markedly improving modeling efficiency and practical applicability of Bayesian networks in complex domains.

Technology Category

• Reasoning under Uncertainty: Causality
• Machine Learning: Causal Learning
• Cognitive Modeling & Cognitive Systems: Conceptual Inference and Reasoning

Application Category

• Graph Algorithms and Modeling for the Web: Algorithms and analysis for incomplete, noisy, or partially observed Web-related graphs
• Social Networks and Social Media: Influence propagation, information diffusion, and the prediction on networks
• User Modeling, Personalization and Recommendation: Explainable and interpretable methods for personalization

Abstract

The very expressiveness of Bayesian networks can introduce fresh challenges due to the large number of relationships they often model. In many domains, it is thus often essential to supplement any available data with elicited expert judgements. This in turn leads to two key challenges: the cognitive burden of these judgements is often very high, and there are a very large number of judgements required to obtain a full probability model. We can mitigate both issues by introducing assumptions such as independence of causal influences (ICI) on the local structures throughout the network, restricting the parameter space of the model. However, the assumption of ICI is often unjustified and overly strong. In this paper, we introduce the surjective independence of causal influences (SICI) model which relaxes the ICI assumption and provides a more viable, practical alternative local structure model that facilitates efficient Bayesian network parameterisation.

Problem

• Relaxing strong independence assumptions in Bayesian networks
• Reducing cognitive burden of expert judgements for parameterisation
• Providing practical local structure alternatives for efficient modelling

Innovation

• Relaxed ICI assumption for local structures
• Introduced surjective independence causal influences
• Enabled efficient Bayesian network parameterisation