Explainable AI (XAI) draws widespread attention, especially in recommendation systems. In this context, counterfactual explanations stand out as an efficient method for providing explanations with actionable insights about how the system makes decisions. The counterfactual explanation is a small set of changing inputs to make different outputs (e.g., by removing or modifying data points). Methods like ACCENT (Action-based Counterfactual Explanations for Neural Recommenders for Tangibility) have proven their ability to generate counterfactual explanations by removing user action. However, these methods suppose the user actions are independent of each other, ignoring the causal relationship between actions, leading to unrealistic counterfactual scenarios. In this work, we propose a new way to improve ACCENT with causality, by integrating causal inference into the process of generating counterfactual explanations. We build a causal data structure to maintain logic and consistency of user interaction history. Specially, when a data point is removed, causally related points will also be adjusted synchronously. Experimental results show that this improved method not only improves the causal percentage but also improves the CF set size, leading to more reliable and interpretable counterfactual explanations.