Graph Topological Aspects of Granger Causal Network Learning

TL;DR

This paper explores the topological properties of causality graphs in stationary time series, introducing conditions for accurate graph recovery and demonstrating efficiency improvements over existing methods.

Contribution

It develops the concept of strongly causal graph topologies for reliable causality inference and provides heuristics that enhance efficiency in practical applications.

Findings

Strongly causal graphs enable pairwise causality testing to recover true causality.

Finite-sample heuristics improve statistical and computational efficiency.

Simulation results show advantages over LASSO-type algorithms.

Abstract

We study Granger causality in the context of wide-sense stationary time series, where our focus is on the topological aspects of the underlying causality graph. We establish sufficient conditions (in particular, we develop the notion of a "strongly causal" graph topology) under which the true causality graph can be recovered via pairwise causality testing alone, and provide examples from the gene regulatory network literature suggesting that our concept of a strongly causal graph may be applicable to this field. We implement and detail finite-sample heuristics derived from our theory, and establish through simulation the efficiency gains (both statistical and computational) which can be obtained (in comparison to LASSO-type algorithms) when structural assumptions are met.