@inproceedings{9de1f4cdbc31405f9a2d7bea2a3a49cb,
title = "Interaction information for causal inference: The case of directed triangle",
abstract = "Interaction information is one of the multivariate generalizations of mutual information, which expresses the amount of information shared among a set of variables, beyond the information shared in any proper subset of those variables. Unlike (conditional) mutual information, which is always non-negative, interaction information can be negative. We utilize this property to find the direction of causal influences among variables in a triangle topology under some mild assumptions.",
keywords = "Causal inference, Interaction information, Mutual information generalization",
author = "Ghassami, {Amir Emad} and Negar Kiyavash",
note = "Funding Information: This work was supported by MURI grant ARMY W911NF-15-1-0479 and ONR grant N00014-16-1-2804. Publisher Copyright: {\textcopyright} 2017 IEEE.; 2017 IEEE International Symposium on Information Theory, ISIT 2017 ; Conference date: 25-06-2017 Through 30-06-2017",
year = "2017",
month = aug,
day = "9",
doi = "10.1109/ISIT.2017.8006744",
language = "English (US)",
series = "IEEE International Symposium on Information Theory - Proceedings",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "1326--1330",
booktitle = "2017 IEEE International Symposium on Information Theory, ISIT 2017",
address = "United States",
}