Y. Sreeraman*, S. Lakshmana Pandian
Intelligent Automation & Soft Computing, Vol.29, No.2, pp. 539-557, 2021, DOI:10.32604/iasc.2021.017256
Abstract Causal relationships in a data play vital role in decision making. Identification of causal association in data is one of the important areas of research in data analytics. Simple correlations between data variables reveal the degree of linear relationship. Partial correlation explains the association between two variables within the control of other related variables. Partial association test explains the causality in data. In this paper a couple of causal relationship discovery strategies are proposed using the design of partial association tree that makes use of partial association test among variables. These decision trees are different from normal decision trees in… More >
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