Open AccessResearch Trend of Causal Machine Learning Method: A Literature Review
Author(s) -
Shindy Arti,
Indriana Hidayah,
Sri Suning Kusumawardhani
Publication year - 2020
Publication title -
ijid (international journal on informatics for development)/international journal on informatics for development
Language(s) - English
Resource type - Journals
eISSN - 2549-7448
pISSN - 2252-7834
DOI - 10.14421/ijid.2020.09208
Subject(s) - machine learning , artificial intelligence , computer science , causality (physics) , context (archaeology) , causal inference , causal model , bayesian network , causal structure , econometrics , mathematics , paleontology , statistics , physics , quantum mechanics , biology
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.