Recently, there has been an increasing interest in knowledge graphs (KGs) of causal relations between events. Such KGs can be used for event analysis and forecasting in a variety of applications. In this paper, we study the problem of enriching an existing causal KG of news events using KG embeddings-based link prediction techniques. We perform a thorough evaluation of the performance of five different methods using classic accuracy measures as well as a novel scheme for manual evaluation. Our study provides insights on the strengths and weaknesses of different link prediction methods.