erdogant/bnlearn
Python package for Causal Discovery by learning the graphical structure of Bayesian networks. Structure Learning, Parameter Learning, Inferences, Sampling methods.
This project helps data analysts and researchers understand the 'why' behind their data by uncovering cause-and-effect relationships. You input a dataset, and it outputs a graphical model showing how different factors influence each other, along with predictions and insights into interventions. This is useful for anyone looking to move beyond simple correlations to true causal understanding.
612 stars. Used by 1 other package. Available on PyPI.
Use this if you need to determine the underlying causal structure of complex systems, make predictions based on interventions, or generate synthetic data that reflects real-world causal links.
Not ideal if you only need simple statistical correlations or if your data is purely observational without any potential for inferring causality.
Stars
612
Forks
56
Language
Jupyter Notebook
License
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Category
Last pushed
Mar 07, 2026
Commits (30d)
0
Dependencies
20
Reverse dependents
1
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