Explainable uplift modeling via linearized kernel feature maps.
Project description
xuplift is a library for explainable uplift modeling. It uses linearized kernel feature maps to estimate treatment effects with both speed and mathematical rigor. Instead of computing a massive $N \times N$ kernel matrix, xuplift selects landmark points to project data into a finite-dimensional feature space.
Supported Models
- Regressor: Kernel-based Ridge regressor for outcome and residual modeling.
- Classifier: Kernel-based Logistic classifier for precise propensity score estimation.
Supported Meta-Learners
- RLearner: Residual-on-residual estimator.
- SLearner: Single-learner approach treating treatment as a feature.
- TLearner: Two-learner approach for baseline causal analysis.
- XLearner: Cross-learner optimized for significantly unbalanced treatment groups.
Installation
pip install xuplift
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