causalml and scikit-uplift
These are competitors offering overlapping uplift modeling functionality, with Causal ML providing a broader suite of causal inference methods while scikit-uplift focuses specifically on scikit-learn API compatibility for practitioners preferring that interface.
About causalml
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.
About scikit-uplift
maks-sh/scikit-uplift
:exclamation: uplift modeling in scikit-learn style in python :snake:
This tool helps marketing specialists, CRM managers, or business analysts identify which customers are most likely to respond positively to a marketing campaign or intervention, and only when treated. It takes historical customer data, including treatment (e.g., received a promotion) and outcome (e.g., made a purchase), to predict the 'uplift' or incremental impact of future actions. The output helps you focus your efforts on the customer segments where your campaigns will have the most significant positive effect.
Related comparisons
Scores updated daily from GitHub, PyPI, and npm data. How scores work