Data-Driven Approach for Inferencing Causality and Network Topology

In this paper, we provide a novel approach to capture causal interaction in a linear dynamical system from time-series data. In [1], we have shown that the existing measures of information transfer, namely directed information, Granger causality and transfer entropy fail to capture true causal interaction in a dynamical system and proposed a new definition of information transfer that captures true causal interaction. The main contribution of this paper is to show that the proposed definition of information transfer in [1] [2] can be computed from time-series data and the computed information measure allows the identification of causal interaction and network topology in a dynamical system. The data-driven algorithm for computation of information transfer for linear systems relies on a robust optimization formulation of transfer operator theoretic framework. The proposed technique is applied to a number of different examples to establish its efficiency.