@inproceedings{d26153e989614793aad0282f9e5244e9,
title = "A Systematic Literature Review in Causal Association Rules Mining",
abstract = "As quoted recently, this is the age of information, and for information we need data. Data is everywhere around us and it is expanding dramatically. The aim of this research is to inspect and summarize the state-of-The-Art approaches and studies of machine learning methods to causal inference techniques. This review utilizes a systematic literature research to the mostly prominent digital database libraries in the field of computer sciences in recent years. The objective is to identify and investigate three raised research questions to broadly analyze and detailly explore several points of view concerning causal association rules and their application in real-world problems.",
keywords = "association rules, causal discovery, causality, data mining",
author = "Shkurte Luma-Osmani and Florije Ismaili and Xhemal Zenuni and Bujar Raufi",
note = "Publisher Copyright: {\textcopyright} 2020 IEEE.; 11th Annual IEEE Information Technology, Electronics and Mobile Communication Conference, IEMCON 2020 ; Conference date: 04-11-2020 Through 07-11-2020",
year = "2020",
month = nov,
day = "4",
doi = "10.1109/IEMCON51383.2020.9284908",
language = "English",
series = "11th Annual IEEE Information Technology, Electronics and Mobile Communication Conference, IEMCON 2020",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "48--54",
editor = "Rajashree Paul",
booktitle = "11th Annual IEEE Information Technology, Electronics and Mobile Communication Conference, IEMCON 2020",
address = "United States",
}