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All Journal IJID (International Journal on Informatics for Development)
Shindy Arti
Department of Electrical Engineering and Technology Information Gadjah Mada University Yogyakarta
Computer Science & IT

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Research Trend of Causal Machine Learning Method: A Literature Review Shindy Arti; Indriana Hidayah; Sri Suning Kusumawardani
IJID (International Journal on Informatics for Development) Vol. 9 No. 2 (2020): IJID December
Publisher : Faculty of Science and Technology, UIN Sunan Kalijaga Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.14421/ijid.2020.09208

Abstract

Machine learning is commonly used to predict and implement  pattern recognition and the relationship between variables. Causal machine learning combines approaches for analyzing the causal impact of intervention on the result, asumming a considerably ambigous variables. The combination technique of causality and machine learning is adequate for predicting and understanding the cause and effect of the results. The aim of this study is a systematic review to identify which causal machine learning approaches are generally used. This paper focuses on what data characteristics are applied to causal machine learning research and how to assess the output of algorithms used in the context of causal machine learning research. The review paper analyzes 20 papers with various approaches. This study categorizes data characteristics based on the type of data, attribute value, and the data dimension. The Bayesian Network (BN) commonly used in the context of causality. Meanwhile, the propensity score is the most extensively used in causality research. The variable value will affect algorithm performance. This review can be as a guide in the selection of a causal machine learning system.
Co-Authors Indriana Hidayah Sri Suning Kusumawardani