This paper focuses on the optimization of the assessment system of comprehensive course design. It uses the switched-mode power supply (SMPS) design competition as an example to discuss using the causal inference method to establish reasonable scoring criteria. This paper reveals the limitations of traditional probability statistics in dealing with complex causal relationships by analyzing the competitors’ performance in different experiments and the “data generating process” behind them and it adopts the causal inference model to assess the competitors’ abilities more accurately. In the research, the competitors’ ability to decompose higher-order problems and the difficulty of experiments are considered potential variables, and the complex interactions between these factors are clarified through constructing causal diagrams, especially in identifying and controlling confounders and mediating variables. On this basis, this paper innovatively proposes an assessment strategy, the Enhanced Causal-Weighted Scoring Method, which aims to account for differences in experimental difficulty while more accurately reflecting competitors’ abilities in higher-order problem decomposition. The advantages of the method in scoring are demonstrated by comparing it to traditional percentage scoring. This method solves the scoring problem and provides a scientific and reasonable educational evaluation strategy for comprehensive course design and related competition fields. In addition, given the current rapid development of artificial intelligence (AI) where AI still lacks causal thinking as well as data generation processes, the results of this research provide the theoretical support and practical orientation containing causal logic for the development of an AI scoring system, which promotes the growth of intelligence and depth of the educational evaluation system.