Causal screening for dynamical systems

TL;DR

This paper introduces inexpensive causal screening methods for dynamical systems that leverage local independence, providing sound causal interpretations under certain assumptions, with applications demonstrated on biological data.

Contribution

It proposes novel causal screening techniques for dynamical systems that are computationally efficient and interpretable, using local independence and ancestral faithfulness assumptions.

Findings

Methods are computationally inexpensive.

Framework performs well on biological system data.

Output tends to be close to complete for sparse graphs.

Abstract

Many classical algorithms output graphical representations of causal structures by testing conditional independence among a set of random variables. In dynamical systems, local independence can be used analogously as a testable implication of the underlying data-generating process. We suggest some inexpensive methods for causal screening which provide output with a sound causal interpretation under the assumption of ancestral faithfulness. The popular model class of linear Hawkes processes is used to provide an example of a dynamical causal model. We argue that for sparse causal graphs the output will often be close to complete. We give examples of this framework and apply it to a challenging biological system.