#15 · Primary category： Classical Machine Learning Frameworks
causalnex
A Python library that helps data scientists to infer causation rather than observing correlation.
Project last updated：08/10/26
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Why we included this project
CausalNex tackles the step most data science libraries skip: turning observational data into a structured causal model rather than a list of correlations. You learn the graph structure from your data, then hand it to domain experts who can edit the relationships before the probability distributions are fitted, and finally run what-if or counterfactual questions to trace how a change in one variable flows through the rest. It comes out of McKinsey's QuantumBlack team, so the workflow leans toward decisions you can act on instead of academic demonstrations. The catch is maintenance: the project reached end of life in June 2026 and now sits as a read-only archive with no bug fixes or security patches coming, which matters if you plan to build anything long-lived on top of it.
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