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CausalNex


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What is CausalNex?

"A toolkit for causal reasoning with Bayesian Networks."

CausalNex aims to become one of the leading libraries for causal reasoning and "what-if" analysis using Bayesian Networks. It helps to simplify the steps:

Why CausalNex?

CausalNex is built on our collective experience to leverage Bayesian Networks to identify causal relationships in data so that we can develop the right interventions from analytics. We developed CausalNex because:

In our experience, a data scientist generally has to use at least 3-4 different open-source libraries before arriving at the final step of finding the right intervention. CausalNex aims to simplify this end-to-end process for causality and counterfactual analysis.

What are the main features of CausalNex?

The main features of this library are:

How do I install CausalNex?

CausalNex is a Python package. To install it, simply run:

pip install causalnex

Use all for a full installation of dependencies:

pip install "causalnex[all]"

See more detailed installation instructions, including how to setup Python virtual environments, in our installation guide and get started with our tutorial.

How do I use CausalNex?

You can find the documentation for the latest stable release here. It explains:

Note: You can find the notebook and markdown files used to build the docs in docs/source.

Can I contribute?

Yes! We'd love you to join us and help us build CausalNex. Check out our contributing documentation.

How do I upgrade CausalNex?

We use SemVer for versioning. The best way to upgrade safely is to check our release notes for any notable breaking changes.

How do I cite CausalNex?

You may click "Cite this repository" under the "About" section of this repository to get the citation information in APA and BibTeX formats.

What licence do you use?

See our LICENSE for more detail.

We're hiring!

Do you want to be part of the team that builds CausalNex and other great products at QuantumBlack? If so, you're in luck! QuantumBlack is currently hiring Machine Learning Engineers who love using data to drive their decisions. Take a look at our open positions and see if you're a fit.