Feature reduction causal network (FRCN): A novel approach for analyzing coupling relationships in radar system

To evaluate radar performance in complex electromagnetic environments, a compact and efficient causal model is required to model such a complex, nonlinear high-stakes problem. Hence, in this paper, we propose a feature reduction causal network (FRCN). Firstly, to determine the number of hidden layer features in the FRCN, a feature extraction strategy is designed using the intrinsic dimension (ID) of raw data as key prior knowledge, thereby reducing modeling complexity and improving computational efficiency. Then, to further reveal the causal relationships between features and the final objective, a Bayesian network (BN) is constructed in the task layer, intuitively showing the coupling relationships through a directed graph and providing interpretability for decisions on high-stakes problems. Moreover, we extend the layer-wise relevance propagation to the BN in the FRCN, enabling bidirectional reasoning throughout the entire process, which is beneficial to understand the model and its behavior in a human-understandable way. In experiments, it is proved that ID plays a significance role in feature number selection. Next, we design a new interpretable evaluation indicator, called decision-specific average edge relevance, to quantify interpretability. Compared to eight representative models, FRCN not only achieves higher accuracy but also provides stronger interpretability in terms of relevance, informativeness, and trustworthiness. A detailed analysis of a radar system enhances the understanding of coupling relationships among various factors, thereby validating the effectiveness of FRCN in feature reduction, interpretability, and trustworthiness for high-dimensional, complex, and nonlinear data.