News recommender systems face inherent challenges from popularity bias, where user interactions concentrate heavily on a small subset of popular news. While existing debiasing methods have made progress in recommendation, they often overlook two critical aspects: the different granularity of news popularity (across titles, categories, etc.) and how hierarchical popularity levels distinctly influence user interest modeling. Hence, in this paper, we propose a hierarchical causal debiasing framework that effectively captures genuine user interests while mitigating popularity bias at different granularity levels. Our framework incorporates two key components during training: (1) a hierarchical popularity-aware user modeling module to capture user interests by distinguishing popular and unpopular interactions at different granularity news content; and (2) a dual-view structure combining counterfactual reasoning for popular-view news with inverse propensity weighting for unpopular-view news to model user genuine interests. During inference, our framework removes popularity-induced effects to predict relatedness between user and candidate news. Extensive experiments on two widely-used datasets, MIND and Adressa, demonstrate that our framework significantly outperforms existing baseline approaches in addressing both the long-tail distribution challenge. Our code is available at \url{https://github.com/hexiangfu123/HPDM}.