causalml and CImpact
Uplift modeling focuses on heterogeneous treatment effects from experimental data, while causal inference for time series addresses temporal dependency structures—making them **complements** that address different causal inference problems (cross-sectional vs. sequential).
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 CImpact
Sanofi-Public/CImpact
Causal inference library for timeseries analysis
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