We investigate the impact of transitive reduction on citation networks. Our hypothesis is that documents which lose fewer citations under transitive reduction are likely to be interdisciplinary, while a large loss of citations suggests a document is primarily cited within a single discipline. We test this hypothesis by using an artificial model of a citation network and by using data on citations from three sources: academic papers, court decisions and patents. Where needed, we applied modularity-based clustering techniques on a network defined using bibliographic coupling to classify documents by topic. A cluster-dependent measure was then used to classify the nodes as interdisciplinary or intradisciplinary. Our results provide strong support for our hypothesis in three of the four cases, with somewhat weaker but still positive support in the case of patents.