causalml and Causalis
Uber/causalml is a mature, production-ready uplift modeling framework that would serve as a direct competitor to Causalis for practitioners seeking robust causal inference methods, though Causalis's specific focus on experimental design robustness might complement rather than replace causalml's broader algorithm coverage if both projects mature.
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 Causalis
causalis-causalcraft/Causalis
Causalis - State-of-the-art robust causal inference for experiments and observational data in python
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