A Novel Evidential Reasoning Rule with Causal Relationships between Evidence

The evidential reasoning (ER) rule framework has been widely applied in multi-attribute decision analysis and system assessment to manage uncertainty. However, traditional ER implementations rely on two critical limitations: 1) unrealistic assumptions of complete evidence independence, and 2) a lack of mechanisms to differentiate causal relationships from spurious correlations. Existing similarity-based approaches often misinterpret interdependent evidence, leading to unreliable decision outcomes. To address these gaps, this study proposes a causality-enhanced ER rule (CER-e) framework with three key methodological innovations: 1) a multidimensional causal representation of evidence to capture dependency structures; 2) probabilistic quantification of causal strength using transfer entropy, a model-free information-theoretic measure; 3) systematic integration of causal parameters into the ER inference process while maintaining evidential objectivity. The PC algorithm is employed during causal discovery to eliminate spurious correlations, ensuring robust causal inference. Case studies in two types of domains—telecommunications network security assessment and structural risk evaluation—validate CER-e’s effectiveness in real-world scenarios. Under simulated incomplete information conditions, the framework demonstrates superior algorithmic robustness compared to traditional ER. Comparative analyses show that CER-e significantly improves both the interpretability of causal relationships and the reliability of assessment results, establishing a novel paradigm for integrating causal inference with evidential reasoning in complex system evaluation.