Learning causal dependencies in large-variate time series

A major challenge in causal inference from observational data is to discriminate between associative dependencies and effective causal relationships. This is particularly challenging in large-variate and temporal settings (e.g. in spatio-temporal time series) where the multivariate nature of interactions induces a significant correlation between most of the variables. In recent years, a number of data-driven approaches have been proposed to learn the mapping between some features of the data distribution and the probability of a causal connection between a pair of variables. Most state-of-the-art approaches, however, deal with bivariate cases neglecting the role of the context determined by the other variables. This is a strong limitation in large-variate and temporal settings which are the object of this study. In order to address the context issue, this paper introduces a new set of descriptors based on interaction information to featurize the context and justifies its introduction by using a graphical modeling formalism. The resulting causal inference method is assessed on a number of large-variate synthetic stationary time series. The assessment shows that the proposed method outperforms several state-of-the-art causal inference techniques.