CausalMGM is a data analysis tool to explore large, complex datasets. The method learns a graphical model of the data where the nodes are variables and edges display the dependencies among variables. The graphical model allows users to query their data to find the direct influences of a target variable of interest, or to find novel associations between pairs of variables.
The CausalMGM method expects a text file in tab-separated format with variables in the columns and samples in the rows.
CausalMGM's feature selection is based on the PrefDiv algorithm, which is a method to identify the features most associated with a target variable yet not associated to one another.
PrefDiv requires the following input: