Sponsor

Tip

You can support this project in various ways ❤️

• Become a Sponsor!

• Buy me a coffee! I ❤️ coffee :)

• Donate in Bitcoin.

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• Star this repo at the github page.

• You can also buy a digital educational product over here.

• Other contributions can be in the form of feature requests, idea discussions, reporting bugs, opening pull requests.

Cheers Mate.

Medium Blog

Note

• Guide in detecting causal relationships using Bayesian Structure Learning in Python.

• Guide in designing knowledge-driven models using Bayesian theorem.

• A Comparative Analysis of Libraries to Reveal Hidden Causality in Your Dataset.

Github

Note

Source code of bnlearn can be found at Github

Colab Notebook

Note

• General functionalities

• Inferences on the salary data sets

• Knowledge driven approach

Citing

The bibtex can be found in the right side menu at the github page.

References

• Probabilistic Graphical Models using pgmpy

• Causality, Pearl, 2009, 2nd Editing

• If correlation doesn’t imply causation, then what does? from Michael Nielsen

• Lecture notes from Jonas Peters

• Elements of Causal Inference

• Causality slides

Related Packages

• Causal graphical models

• Causality

• Causal Inference

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