Micro-dynamics analysis plays an important role in decision making in a complex social system. It has been used to analyze how macro-phenomena arise from the viewpoint of individual agent behavior. However, the causes extracted during the analysis often include two types of useless causes: simple causes, which are not useful for decision making regarding new policies, and small causes, which suggest inefficient policies. In this paper, we propose a method to extract causes that include at least one feature from the attribute, perception, and action variables of model parameters and logs. We extracted the causes of the specific congestion and created a policy based on results obtained via a simulation of an airport terminal and showed that the proposed method can eliminate both simple and small causes.