Interventional dependency graphs: An approach for discovering influence structure

Jalal Etesami; Negar Kiyavash

doi:10.1109/ISIT.2016.7541481

Interventional dependency graphs: An approach for discovering influence structure

Jalal Etesami, Negar Kiyavash

University of Illinois Urbana-Champaign

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

Abstract

In this paper, we introduce a new type of graphical model, interventional dependency graphs, to encode interactions among processes. These type of graphical models are defined using a measure that captures the influence relationships based on the principle of intervention. Principle of intervention discovers an influence relationship by making assignment to certain variables while fixing other variables to see how these changes influence statistics of variables of interest. Furthermore, we derive some properties of the dynamics that can be inferred from these graphs and establish the relationship between this new graphical model and the directed information graphs used for causal inference.

Original language	English
Title of host publication	Proceedings - ISIT 2016; 2016 IEEE International Symposium on Information Theory
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	1158-1162
Number of pages	5
ISBN (Electronic)	9781509018062
DOIs	https://doi.org/10.1109/ISIT.2016.7541481
State	Published - 10 Aug 2016
Externally published	Yes
Event	2016 IEEE International Symposium on Information Theory, ISIT 2016 - Barcelona, Spain Duration: 10 Jul 2016 → 15 Jul 2016

Publication series

Name	IEEE International Symposium on Information Theory - Proceedings
Volume	2016-August
ISSN (Print)	2157-8095

Conference

Conference	2016 IEEE International Symposium on Information Theory, ISIT 2016
Country/Territory	Spain
City	Barcelona
Period	10/07/16 → 15/07/16

Access to Document

10.1109/ISIT.2016.7541481

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Etesami, J., & Kiyavash, N. (2016). Interventional dependency graphs: An approach for discovering influence structure. In Proceedings - ISIT 2016; 2016 IEEE International Symposium on Information Theory (pp. 1158-1162). Article 7541481 (IEEE International Symposium on Information Theory - Proceedings; Vol. 2016-August). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ISIT.2016.7541481

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title = "Interventional dependency graphs: An approach for discovering influence structure",

abstract = "In this paper, we introduce a new type of graphical model, interventional dependency graphs, to encode interactions among processes. These type of graphical models are defined using a measure that captures the influence relationships based on the principle of intervention. Principle of intervention discovers an influence relationship by making assignment to certain variables while fixing other variables to see how these changes influence statistics of variables of interest. Furthermore, we derive some properties of the dynamics that can be inferred from these graphs and establish the relationship between this new graphical model and the directed information graphs used for causal inference.",

author = "Jalal Etesami and Negar Kiyavash",

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year = "2016",

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doi = "10.1109/ISIT.2016.7541481",

language = "English",

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Etesami, J & Kiyavash, N 2016, Interventional dependency graphs: An approach for discovering influence structure. in Proceedings - ISIT 2016; 2016 IEEE International Symposium on Information Theory., 7541481, IEEE International Symposium on Information Theory - Proceedings, vol. 2016-August, Institute of Electrical and Electronics Engineers Inc., pp. 1158-1162, 2016 IEEE International Symposium on Information Theory, ISIT 2016, Barcelona, Spain, 10/07/16. https://doi.org/10.1109/ISIT.2016.7541481

Interventional dependency graphs: An approach for discovering influence structure. / Etesami, Jalal; Kiyavash, Negar.
Proceedings - ISIT 2016; 2016 IEEE International Symposium on Information Theory. Institute of Electrical and Electronics Engineers Inc., 2016. p. 1158-1162 7541481 (IEEE International Symposium on Information Theory - Proceedings; Vol. 2016-August).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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N2 - In this paper, we introduce a new type of graphical model, interventional dependency graphs, to encode interactions among processes. These type of graphical models are defined using a measure that captures the influence relationships based on the principle of intervention. Principle of intervention discovers an influence relationship by making assignment to certain variables while fixing other variables to see how these changes influence statistics of variables of interest. Furthermore, we derive some properties of the dynamics that can be inferred from these graphs and establish the relationship between this new graphical model and the directed information graphs used for causal inference.

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Etesami J, Kiyavash N. Interventional dependency graphs: An approach for discovering influence structure. In Proceedings - ISIT 2016; 2016 IEEE International Symposium on Information Theory. Institute of Electrical and Electronics Engineers Inc. 2016. p. 1158-1162. 7541481. (IEEE International Symposium on Information Theory - Proceedings). doi: 10.1109/ISIT.2016.7541481

Interventional dependency graphs: An approach for discovering influence structure

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