A minimal approach to causal inference on topologies with bounded indegree

Christopher Quinn, Negar Kiyavash, Todd Coleman

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

The structure of the causal interdependencies between processes in a causal, stochastic dynamical system can be succinctly characterized by a generative model. Inferring the structure of the generative model, however, requires calculating divergences using the full joint statistics. For the case when an upperbound on the indegree of each process is known, we describe a computationally efficient method using directed information which does not require the full statistics and recovers the parents of each process independently from finding the parents of other processes.

Original languageEnglish (US)
Title of host publication2011 50th IEEE Conference on Decision and Control and European Control Conference, CDC-ECC 2011
Pages168-173
Number of pages6
DOIs
StatePublished - Dec 1 2011
Event2011 50th IEEE Conference on Decision and Control and European Control Conference, CDC-ECC 2011 - Orlando, FL, United States
Duration: Dec 12 2011Dec 15 2011

Publication series

NameProceedings of the IEEE Conference on Decision and Control
ISSN (Print)0191-2216

Other

Other2011 50th IEEE Conference on Decision and Control and European Control Conference, CDC-ECC 2011
CountryUnited States
CityOrlando, FL
Period12/12/1112/15/11

ASJC Scopus subject areas

  • Control and Systems Engineering
  • Modeling and Simulation
  • Control and Optimization

Fingerprint Dive into the research topics of 'A minimal approach to causal inference on topologies with bounded indegree'. Together they form a unique fingerprint.

  • Cite this

    Quinn, C., Kiyavash, N., & Coleman, T. (2011). A minimal approach to causal inference on topologies with bounded indegree. In 2011 50th IEEE Conference on Decision and Control and European Control Conference, CDC-ECC 2011 (pp. 168-173). [6161255] (Proceedings of the IEEE Conference on Decision and Control). https://doi.org/10.1109/CDC.2011.6161255