uber/causalml
Uplift modeling and causal inference with machine learning algorithms
This project helps marketers and data analysts understand the true impact of different actions on customer behavior. By analyzing experimental or historical data, it tells you which specific customers are most likely to respond positively to an ad campaign or a personalized product recommendation. The output is a clear estimate of how each individual customer will react to an intervention.
5,758 stars. Used by 1 other package. Actively maintained with 3 commits in the last 30 days. Available on PyPI.
Use this if you need to identify which customers will respond most favorably to a specific campaign or personalized offering to maximize your return on investment.
Not ideal if you're looking for simple A/B test result interpretation without needing to understand individual customer responses.
Stars
5,758
Forks
852
Language
Python
License
—
Category
Last pushed
Mar 07, 2026
Commits (30d)
3
Dependencies
18
Reverse dependents
1
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