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
Implements multiple meta-learner algorithms (S-learner, T-learner, X-learner, R-learner, doubly robust methods) and tree-based approaches for heterogeneous treatment effect estimation from both experimental and observational data. Provides a unified interface for CATE estimation with built-in support for feature selection, multi-treatment scenarios, and cost optimization. Integrates with scikit-learn estimators and supports both traditional ML and deep learning backends for flexible model composition.
About scikit-uplift
maks-sh/scikit-uplift
:exclamation: uplift modeling in scikit-learn style in python :snake:
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