Learning multivariate time series causal graphs based on conditional mutual information

Abstract

Detection and clarification of cause-effect relationships among variables is an important problem in time series analysis. This paper provides a method that employs both mutual information and conditional mutual information to identify the causal structure of multivariate time series causal graphical models. A three-step procedure is developed to learn the contemporaneous and the lagged causal relationships of time series causal graphs. Contrary to conventional constraint-based algorithm, the proposed algorithm does not involve any special kinds of distribution and is nonparametric. These properties are especially appealing for inference of time series causal graphs when the prior knowledge about the data model is not available. Simulations and case analysis demonstrate the effectiveness of the method.