Measuring causal relationships in dynamical systems through recovery of functional dependencies

Jalal Etesami, Negar Kiyavash

Research output: Contribution to journalArticlepeer-review


We introduce a measure of causality that captures the functional dependencies in dynamical systems and subsequently, define a new type of graphical model, functional dependency graph, to encode such dependencies. We study the relationship between this type of graphical model and other graphical models such as directed information graphs and linear dynamical graphs that have been proposed to capture causal influences in dynamical systems. We show that functional dependency graphs are a generalization of these previously introduced graphical models and learn the functional dependencies in a larger class of models. We also establish sufficient conditions under which the functional dependency graph defined through our measure is equivalent to the directed information graphs. Some simulation results on linear and nonlinear dynamics are provided.

Original languageEnglish (US)
Article number7782866
Pages (from-to)650-659
Number of pages10
JournalIEEE Transactions on Signal and Information Processing over Networks
Issue number4
StatePublished - Dec 2017


  • Causal structure
  • functional dependency
  • graphical model
  • statistical dependency

ASJC Scopus subject areas

  • Signal Processing
  • Information Systems
  • Computer Networks and Communications


Dive into the research topics of 'Measuring causal relationships in dynamical systems through recovery of functional dependencies'. Together they form a unique fingerprint.

Cite this