In sequential recommender systems, two primary challenges are the long-tailed distribution of data and data distribution bias. To effectively address these issues, a Contrastive and Causal Learning Algorithm for Sequential Recommendation (C2ASeRec) has been proposed. The algorithm enhances the training efficacy of sequential recommendation models and boosts their performance by introducing environment partition and reweighting, regularization term constraint based on causal learning, and methods to enhance uniformity of representation. These innovations mitigate the performance degradation previously caused by data distribution bias. By concurrently incorporating causal learning-based regularization constraints and representation uniformity enhancement techniques, C2ASeRec demonstrates both universality and robustness across different environment partitioning principles, enabling superior performance in complex real-world scenarios. Experimental results indicate that C2ASeRec achieves outstanding outcomes in addressing data distribution bias. In terms of key performance metrics such as hit rate and normalized discounted cumulative gain, our algorithm significantly surpasses seven previous methods, showcasing exceptional advanced performance.