causalml and causeinfer
Uber's mature, production-focused library and andrewtavis's smaller implementation represent competitors in the same problem space, where practitioners would typically choose CausalML for its broader algorithm coverage and maintained infrastructure rather than use both simultaneously.
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 causeinfer
andrewtavis/causeinfer
Machine learning based causal inference/uplift in Python
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