FenTechSolutions/CausalDiscoveryToolbox
Package for causal inference in graphs and in the pairwise settings. Tools for graph structure recovery and dependencies are included.
This tool helps data scientists and researchers uncover cause-and-effect relationships within their datasets. You provide observational data, and it outputs a graph that illustrates how different variables influence each other. It's designed for anyone who needs to understand the underlying causal structure of complex systems.
1,226 stars. No commits in the last 6 months.
Use this if you have observational data and need to build a graphical model to understand which variables causally influence others.
Not ideal if you're looking for a simple correlation analysis or a tool that directly performs interventional experiments without causal discovery.
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1,226
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Language
Python
License
MIT
Category
Last pushed
Oct 13, 2025
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