CausalNex: An open-source Python library that helps data scientists to infer causation rather than observing correlation

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

Image source: https://medium.com/@QuantumBlack/introducing-causalnex-driving-models-which-respect-cause-and-effect-a561545f0a5e

Installation

CausalNex is a Python package. Run it:

pip install causalnex

GitHub: 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

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