CausalNex is a Python library that uses Bayesian Networks to combine machine learning and domain expertise for causal reasoning. You can use CausalNex to uncover structural relationships in your data, learn complex distributions, and observe the effect of potential interventions.
Main features of CausalNex¶
The CausalNex library has the following features:
Deploys state-of-the-art structure learning method, DAG with NO TEARS, to understand conditional dependencies between variables
Allows domain knowledge to augment model relationships
Builds predictive models based on structural relationships
Understands model probability
Evaluates model quality with standard statistical checks
Visualisation which simplifies how causality is understood
Analyses the impact of interventions using Do-calculus
Learning About CausalNex¶
In the next few chapters, you will learn how to install and set up CausalNex, and how to use it on your own projects. Once you are set up, to get a feel for CausalNex, we suggest working through our example tutorial project. Advanced users looking for in-depth information should consult the User Guide. You can also check out the resources section for answers to frequently asked questions and the API reference documentation for further, detailed information.
We have designed the documentation in general, and the tutorial in particular, for beginners to get started using Bayesian Networks on their projects. If you an have elementary knowledge of Python and Bayesian Networks then you may find the CausalNex learning curve more challenging. However, we have simplified the tutorial by providing all the Python functions necessary to create your first CausalNex project.
Note: There are a number of excellent online resources for learning Python, but be aware that you should choose those that reference Python 3, as CausalNex is built for Python 3.6+. There are many curated lists of online resources, such as:
There are also several excellent online resources for learning about Bayesian Networks, such as:
Lecture notes on Probabilistic graphical models based on Stanford CS228;
An Introduction to Bayesian Network Theory and Usage by T. Stephenson;