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There has been increasing interest in the study of research communities with the goal of optimizing their outcomes and impact. While current methods can predict future trends, they offer little insight about the causes of the trends. However, causal insight is important for strategic decision making to improve a community. This paper presents a new method to predict the possible causes for inefficiencies in a community by relating them to disconnections between trends, like trends in the number of publications, patents, citations, and so on. The method combines traditional scientometric and webometric metrics and metric predictions with a recent model for trend analysis in a community. The proposed method was used to analyze electronic design automation (EDA) domain. The analysis showed intriguing disconnections between the trends of the number of papers, number of granted patents, and impact of its main …