Causal feature selection has attracted much attention in recent years, as the causal features selected imply the causal mechanism related to the class attribute, leading to more reliable prediction models built using them. Currently there is a need of developing multi-source feature selection methods, since in many applications data for studying the same problem has been collected from various sources, such as multiple gene expression datasets obtained from different experiments for studying the causes of the same disease. However, the state-of-the-art causal feature selection methods generally tackle a single dataset, and a direct application of the methods to multiple datasets will result in unreliable results as the datasets may have different distributions. To address the challenges, by utilizing the concept of causal invariance in causal inference, we first formulate the problem of causal feature selection with multiple datasets as a search problem for an invariant set across the datasets, then give the upper and lower bounds of the invariant set, and finally we propose a new Multi-source Causal Feature Selection algorithm, MCFS. Using synthetic and real world datasets and 16 feature selection methods, the extensive experiments have validated the effectiveness of MCFS.