Event causality identification (ECI) is an important task in natural language processing (NLP) which aims to identify the causal relationships between events in text pieces, i.e., predict whether one event causes another one to happen. Due to the diversity of real-world causality events and difficulty in obtaining sufficient training data, existing ECI approaches have poor generalizability and struggle to identify the relation between seldom-seen events. We propose to utilize both external knowledge and internal analogy to improve ECI. By utilizing a commonsense knowledge graph to reveal the commonalities or associations between different events, and retrieving similar events as analogy examples to glean useful experiences from such analogous neighbors, we can better identify the relationship between a new event pair. Extensive evaluations show that our approach significantly outperforms other baseline methods.