Metadata-Version: 2.4
Name: xuplift
Version: 0.0.1
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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Explainable uplift modeling via linearized kernel feature maps, providing a collection of meta-learners.

# Installation
Install using pip:
```bash
pip install xuplift
```

# Features
- Regressor: High-performance regression engine for outcome and residual modeling.
- Classifier: Optimized binary classifier for precise propensity score estimation.
- RLearner: Advanced residual-on-residual estimator with built-in 2-fold cross-fitting to ensure unbiased treatment effect estimation.
- XLearner: Optimized cross-learner designed to handle significantly unbalanced treatment groups.
- TLearner/SLearner: Standard two-model and single-model estimators for baseline causal analysis.

