Technology has impacted extensively on the operations of financial markets which are inhabited by a rich array of fixed-income securities, each bearing a particular rate of interest. The relationship between the yields on these various securities is the province of the term structure of interest rates literature which has a long history and can be traced-back formally to Keynes. With the popularity of cointegration and VAR/SVAR approaches to estimation in econometrics, a separate literature using these approaches to estimate and test term structure models and implications has developed. Here the papers are typically motivated by a concern to understand the term structure for the related monetary policy control issues and focus either upon technical estimation issues and often the validity of inferences derived including, importantly, causal inference, the effects of structural change or the testing of various hypotheses. Causality is a particularly important and popular issue given the role of monetary policy intervention. In this paper we wish to add a significant extra dimension to the debate by using graphical modelling to identify causal mechanisms within multivariate time series models. This paper considers an application to the term structure of interest rates where little consensus seems to exist on the causal nexus and direction between long and short rates of interest. In particular, there are three alternative views on causality; short rates cause long rates (broadly the traditional Expectations Hypothesis view); long rates cause short rates (here rational inflation expectations have a role); or the market segmentation, or preferred habitat approaches, where causality is discontinuous across maturity periods. The outcome in an empirical sense will be crucial for the efficacy of monetary policy design and implementation.