Causation is one of the crucial relationships among the related variables that provide good stuff for data analytics. A causal relationship among a set of events exists when one or group of event/s is the result of the occurrence of the other event or a set of events. The primary objective of any research in data analytics or scientific analysis is to identify the level to which a relation exists among the subjective variables. Causal research can facilitate business environment to quantify the effect of present business practices on future production levels to aid in the business planning process. The process of discovering causal relationships among variables have multitude application areas like critical care services in medicine, advertising, bioinformatics, road safety ,share markets, and too more to be included. The present work targeted to study the existing methods of causal relationship discovery. The study also tried to propose the automated and straight forward causal relationship discovery methods which are scalable.

Decision tree, causal relationship,bayesian netorks,Structural Equation models,CDT

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