Recently, reinforcement learning techniques have achieved success in sequence recommendation systems, as they can learn effective recommendation strategies from long-term user feedback signals. However, the design of the model’s reward function faces the challenge of low discriminability. This limits the model’s ability to learn the value differences between different user feedback signals, leading to suboptimal recommendation strategies. Existing studies mainly ensure discriminability of the reward function by adjusting decay factors, but this relies on expert prior knowledge and lacks a theoretical foundation. In order to more reasonably design the reward function and enhance its discriminability, this study analyzes the recommendation system based on counterfactual reasoning and proposes a sequence recommendation algorithm CAL4Rec based on counterfactual discriminability enhancement. Firstly, the proposed method uses structural causal graphs to describe the sequence recommendation process and creatively defines causally identifiable value reward discriminability using causal graphs. Secondly, this method uses a counterfactual generative adversarial self-supervised learning process to optimize the recommendation strategy network and learn the user’s true preferences. Extensive comparative and ablation experiments were conducted on a series of sequence recommendation benchmark datasets for CAL4Rec, and the experimental results show that CAL4Rec’s improvement is effective for various network implementation structures (average 2.34%).