A Survey on Causal Discovery Methods for Temporal and Non-Temporal Data
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Date
2023-04-05
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Attribution 4.0 International (CC BY 4.0)
Attribution 4.0 International (CC BY 4.0)
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Abstract
Causal Discovery (CD) is the process of identifying the cause-effect relationships among
the variables of a system from data. Over the years, several methods have been developed
primarily based on the statistical properties of data to uncover the underlying causal mechanism. In this study, we present an extensive discussion on the methods designed to perform
causal discovery from both independent and identically distributed (i.i.d.) data and time
series data. For this purpose, we first introduce the common terminologies in causal discovery, and then provide a comprehensive discussion of the algorithms designed to identify
the causal edges in different settings. We further discuss some of the benchmark datasets
available for evaluating the performance of the causal discovery methods, available tools
or software packages to perform causal discovery readily, and the common metrics used to
evaluate these methods. We also test some common causal discovery algorithms on different
benchmark datasets, and compare their performances. Finally, we conclude by presenting
the common challenges involved in causal discovery, and also, discuss the applications of
causal discovery in multiple areas of interest.