In the realm of personalized news recommendations (NR), prevailing approaches assist users in discovering content of interest, where user preferences are assumed to be invariant. Unfortunately, violations of such assumptions are common in realistic scenarios with shifted user preferences. For example, concerning sports news, users in South America typically tend to be interested in football, whereas basketball attracts more interest in North America. To bridge this gap, we contribute a novel NR problem named Generalizable NR against Shifted Preference (GNR-SP) in this paper by allowing shifted user preferences. From a causal perspective, we address GNR-SP by disentangling representations of news content and user’s preference, where popularity serves as the observed confounder that influences both semantic content and users’ preferences simultaneously. To this end, we propose a Causal Disentanglement for News Recommendation (CDNR) framework by optimizing a Transformer-based Identifiable Variational Autoencoder (T-iVAE). Our experiments on two real-world datasets showcase the efficacy of our model in handling news recommendations against preference shifts.