mckinsey/causalnex
A Python library that helps data scientists to infer causation rather than observing correlation.
This helps data scientists move beyond simple correlations to understand true cause-and-effect relationships in their data. By inputting various data points and optionally adding expert insights, you can determine what interventions will actually lead to desired outcomes, rather than just observing patterns. It's designed for data scientists who need to build robust models for 'what-if' analysis.
2,445 stars. No commits in the last 6 months.
Use this if you are a data scientist who needs to understand the true causal impact of different factors and identify effective interventions, rather than just finding correlations.
Not ideal if you are looking for a simple predictive model based purely on pattern recognition without needing to understand underlying causal mechanisms.
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Python
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Last pushed
Jun 26, 2024
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