CausalNex is a Python library that allows data scientists and domain experts to co-develop models that go beyond correlation and consider causal relationships. ‘CasualNex’ provides a practical ‘what if’ library which is deployed to test scenarios using Bayesian Networks (BNs).
‘CasualNex’ prepares practitioners to understand structural relationships from data and helps in the verification for accuracy of the relationships between different data sets. Apart from practitioners understanding the structural relationship from data, it also enables domain experts to fit conditional probability distributions and study the effect of potential interventions.
‘CasualNex’ helps to simplify the following steps:
- To learn causal structures,
- To allow domain experts to augment the relationships,
- To estimate the effects of potential interventions using data.
Understanding The Why Behind The Data
Installation
CausalNex is a Python package. Run it:
pip install causalnexGitHub: https://github.com/quantumblacklabs/causalnex
Documentation: https://causalnex.readthedocs.io/
Related Paper: https://papers.nips.cc/paper/8157-dags-with-no-tears-continuous-optimization-for-structure-learning.pdf
Asif Razzaq is the CEO of Marktechpost, LLC. As a visionary entrepreneur and engineer, Asif is committed to harnessing the potential of Artificial Intelligence for social good. His most recent endeavor is the launch of an Artificial Intelligence Media Platform, Marktechpost, which stands out for its in-depth coverage of machine learning and deep learning news that is both technically sound and easily understandable by a wide audience. The platform boasts of over a million monthly views, illustrating its popularity among audiences.
