Using CausalMGM is easy.Just follow the steps below.

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.

Need help with the platform? Introduction Video

Manage Your Data

First, let's upload your dataset.

Once uploaded, your dataset format will be varified.
Use Check My Data to theck the data format. Needs help with the data Format? Already have your task ID?
Upload Data Check My Data Explain Data Format

Do not have datasets?

Get Sample Data Use Sample Data Retrieve Results

Config your experiment.

Please specify the value for each parameter.

Lambda 1:   
     Lambda 2:  

  Lambda 3:  

   Enable PC-Stable?   



No results avaliable.


Interface & Cloud Construction:

Xiaoyu Ge, Daniel Petrov

CausalMGM Implimentation:

Vineet Raghu

Supervised By :

Panos K. Chrysanthis
Panayiotis V. Benos

Data Format:

The CausalMGM method expects a text file in tab-separated format with variables in the columns and samples in the rows. The first row of the file should have the variable names, and each row following should have numerical or categorical data. Numeric columns should only contain digits along with a single decimal point. Categorical columns should only have a maximum of 5 unique categories and may contain any combination of numbers and characters to encode each category. The current implementation of MGM does not support data with missing values, so this should be handled by the user before submitting. Otherwise, complete case analysis or median imputation will be performed automatically. If the user chooses to use our automated methods for handling missing data, then missing data entries should be encoded with an *. Please download the sample data to see an example of the properly formatted dataset.

Introduction to CausalMGM