causalml and doubleml-for-py
These are complements: CausalML provides a broader toolkit for uplift modeling and heterogeneous treatment effects across multiple algorithms, while DoubleML specializes in the double/debiased machine learning framework—a specific methodological approach that could be integrated into or used alongside CausalML's pipeline for more rigorous causal inference.
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 doubleml-for-py
DoubleML/doubleml-for-py
DoubleML - Double Machine Learning in Python
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