In the quest for accurate and interpretable AI models, eXplainable AI (XAI)
has become crucial. Fuzzy Cognitive Maps (FCMs) stand out as an advanced XAI
method because of their ability to synergistically combine and exploit both
expert knowledge and data-driven insights, providing transparency and intrinsic
interpretability. This letter introduces and investigates the "Total Causal
Effect Calculation for FCMs" (TCEC-FCM) algorithm, an innovative approach that,
for the first time, enables the efficient calculation of total causal effects
among concepts in large-scale FCMs by leveraging binary search and graph
traversal techniques, thereby overcoming the challenge of exhaustive causal
path exploration that hinder existing methods. We evaluate the proposed method
across various synthetic FCMs that demonstrate TCEC-FCM's superior performance
over exhaustive methods, marking a significant advancement in causal effect
analysis within FCMs, thus broadening their usability for modern complex XAI
applications.

总因果效应计算，通过二分搜索和图遍历技术，突破了现有方法所困扰的遍历问题，在大规模 Fuzzy Cognitive Maps 中实现了高效的因果效应计算，表明了该方法在因果效应分析中的卓越性能，有助于其在现代复杂的可解释性人工智能应用中的广泛应用。