## 5 Discussion  
Figure 6 shows that among the 15 key influencing factors, three are the direct causes of the target variable, and 10 are included in the second-order local causal network of the target variable. Among them, the three direct causes of the target variable have a great impact on the causal pathway pointing to the target variable in the global causal network, and the normalized causal pathway contribution degree is 19.97%, 18.60%, and 7.11%, respectively, which also indicates that the direct cause node on the Markov blanket has a decisive impact on its corresponding target variable. In combination with Figures 5 and 6, we can also conclude that some indirect causes also have higher causal pathway contribution degrees to the target variable, and a few indirect causes have greater causal pathway contribution degrees to the target variable than other direct causes. For example, the two indirect causes numbered 215 and 95 have a higher causal pathway contribution degree to the target variable than the three direct causes numbered 55, 106 and 277. Among them, the normalized causal pathway contribution degree of node 215 is 11.86%, and the normalized causal pathway contribution degree of node 95 is 11.49%. By comparing Figures 6 and 7, we see that when only the number of causal pathways is considered, the key factors influencing the target variable are basically consistent with those when the number of causal pathways and the length of causal pathways are considered simultaneously. In both results, only one factor changed: Node No. 72, which ranked 15 th in Figure 6, was changed to Node No. 4, which ranked 15 th in Figure 7. However, the ranking of key influencing factors in the two results changed significantly: Only the ranking of node 51 in the 8 th ranking and node 6 in the 13 th ranking remain unchanged. Considering that the influence of the cause variable on the target variable gradually decreases with the length growth of the causal pathway, it is more scientific to calculate the contribution degree of the causal pathway by comprehensively considering the number of causal pathways and the length of causal pathways. From the experimental results in Section 4.6, when all characteristic variables are taken as inputs to the classifier, the prediction performance of the classifier is relatively poor, and F1 = 0.4216 and MCC = 0.0612. When we use the selected 15 key influencing factors as characteristic variables, the prediction performance of the classifier is greatly improved, and F1 = 0.6431 and MCC = 0.2209. This also confirms the theoretical basis of feature selection: For a given sample size, there is a maximum number of features above which the performance of our classifier degrades rather than improves in most cases, and the additional information that is lost by discarding some features is (more than) compensated by a more accurate mapping in the lower-dimensional space. As seen from Figure 8, when we successively delete the key influencing factors ranked 1-15 and take the remaining 14 key influencing factors as the feature variables, the prediction performance of the classifier is gradually improved--the values of F1 and MCC both gradually increase. This also shows from another aspect that there are indeed differences in the specific impacts of the selected key influencing factors on the system. The higher the ranking of key influencing factors is, the greater the corresponding impact on the system. In general, simulation experiments based on the SECOM dataset obtained causal networks among variables that drive the dataset generation. Based on the heuristic causal inference method proposed in this paper, several factors that have a key impact on product quality were identified. This achievement has a certain guiding significance for understanding the monitoring data in semiconductor production. In an ideal situation, the overall operation of the production line can be determined by analysing the monitoring data corresponding to the direct cause of the target variable.