mckinsey/causalnex
A Python library for causal inference and discovery using Bayesian networks, enabling data scientists to model cause-effect relationships.
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CausalNex is a Python library developed by McKinsey’s QuantumBlack that helps data scientists infer causation rather than just observing correlation. It implements Bayesian network-based methods for causal discovery and inference, providing tools for structure learning, model fitting, and causal effect estimation. The library bridges traditional statistical causal analysis with modern machine learning approaches.
Frequently asked
- What is mckinsey/causalnex?
- A Python library for causal inference and discovery using Bayesian networks, enabling data scientists to model cause-effect relationships.
- Is causalnex open source?
- Yes — mckinsey/causalnex is an open-source project tracked on heatdrop.
- What language is causalnex written in?
- mckinsey/causalnex is primarily written in Python.
- How popular is causalnex?
- mckinsey/causalnex has 2.5k stars on GitHub.
- Where can I find causalnex?
- mckinsey/causalnex is on GitHub at https://github.com/mckinsey/causalnex.