• Conditional independence;
  • Counting processes;
  • Event history analysis;
  • Granger causality;
  • Graphoid;
  • Multistate models

Summary.  A new class of graphical models capturing the dependence structure of events that occur in time is proposed. The graphs represent so-called local independences, meaning that the intensities of certain types of events are independent of some (but not necessarilly all) events in the past. This dynamic concept of independence is asymmetric, similar to Granger non-causality, so the corresponding local independence graphs differ considerably from classical graphical models. Hence a new notion of graph separation, which is called δ-separation, is introduced and implications for the underlying model as well as for likelihood inference are explored. Benefits regarding facilitation of reasoning about and understanding of dynamic dependences as well as computational simplifications are discussed.