Inferring extended summary causal graphs from observational time series

TL;DR

This paper introduces new algorithms for learning extended summary causal graphs from observational time series data using information-theoretic measures within a constraint-based framework, demonstrated on simulated and real datasets.

Contribution

It generalizes causation entropy measures for lagged and instantaneous relations and adapts existing algorithms (PC and FCI) for extended causal graph inference.

Findings

Effective on simulated datasets

Applicable to real-world time series

Improves causal discovery accuracy

Abstract

This study addresses the problem of learning an extended summary causal graph on time series. The algorithms we propose fit within the well-known constraint-based framework for causal discovery and make use of information-theoretic measures to determine (in)dependencies between time series. We first introduce generalizations of the causation entropy measure to any lagged or instantaneous relations, prior to using this measure to construct extended summary causal graphs by adapting two well-known algorithms, namely PC and FCI. The behavior of our methods is illustrated through several experiments run on simulated and real datasets.