Biomedical relation extraction is a core problem in biomedical natural language processing, whose goal is to classify the relations between entity mentions within a classified given text, being modeled as a classification method. There has been some recent work on converting biomedical RE to other auxiliary tasks to deal with it. However, they have all neglected the fact that this form of RE is subject to the inherent bias of the auxiliary task, resulting in the inability to make truly reasonable predictions. In this paper, we propose the CouBRE method, which excludes the direct influence of premise-only and hypothesis-only branches in the NLI task when converting biomedical RE to NLI task from a causal perspective on the RE results, allowing the NLI model to make valid predictions based on the combined information of premise and hypothesis rather than based on shortcut paths. Also by removing sample selection bias and label bias in the dataset, CouBRE can make unbiased prediction results more clearly. Comprehensive experiments demonstrate the effectiveness of our approach in low-resource scenarios, outperforming previous state-of-the-art models and remaining competitive even in full-shot scenarios.