Exploring Causal Relations In Data Mining By Using Directed Acyclic Graphs (DAG)
Discovering causal pattern among the set of variables in a database is the primary task of data mining thereby well
predicted models can be developed for decision making. This research article offers a review of open source and easy to use
software package Tetrad used to explore the causal relations in terms of a diagram directed acyclic graph (DAG). Causal
modeling through directed acyclic graphs is a powerful analytic tool that explores the cause and effect relationship between
study variables especially in the time series data that encompass of higher variations. DAG modeling can be seen as an
alternative to many of the statistical procedures like path analysis or structural equation modeling (SEM).
Keywords- Causal Modeling, Decision Making, Directed Acyclic Graph, Tetrad