Metadata-Version: 2.4
Name: xuplift
Version: 0.1.2
Requires-Dist: numpy>=1.24.4
License-File: LICENSE
Summary: Explainable uplift modeling via linearized kernel feature maps.
Author-email: RektPunk <rektpunk@gmail.com>
Requires-Python: >=3.8
Description-Content-Type: text/markdown; charset=UTF-8; variant=GFM
Project-URL: repository, https://github.com/RektPunk/xuplift

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**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
- DRLearner: Doubly robust estimator combining propensity scores and outcome models.
- GRLearner: Generalized R-learner supporting both continuous and binary treatments.
- MLearner: Modified covariates learner optimized for randomized controlled trials (RCT).
- PWLearner: Propensity score weighted learner using inverse probability weighting.
- RLearner: Residual learner minimizing an R-objective via residual-on-residual regression.
- SLearner: Single learner treating treatment assignment as a standard feature.
- TLearner: Two learner approach fitting independent models for each group.
- XLearner: Cross learner optimized for significantly unbalanced treatment groups.

# Installation
```bash
pip install xuplift
```

