scikit-uplift

Here are 2 public repositories matching this topic...

firefly1248 / criteo-uplift-modeling

Reproducible benchmark of uplift-modeling approaches (meta-learners, causal forests, DML, IV) on the Criteo Uplift dataset.

python benchmark jupyter-notebook lightgbm causal-inference criteo uplift-modeling catboost causalml heterogeneous-treatment-effects double-machine-learning meta-learners treatment-effect econml causal-ml scikit-uplift

Updated Jul 3, 2026
Jupyter Notebook

sundar139 / Causal-Uplift-Transformer-Beam

Star

Production-grade causal uplift modeling on 14M rows, benchmarks S-Learner, T-Learner, and FT-Transformer challengers on the Criteo dataset, with Optuna tuning, MLflow tracking, FastAPI + Docker + Google Cloud Run serving, and a Streamlit dashboard.

python docker hyperparameter-tuning causal-inference uplift-modeling mlflow github-actions fastapi optuna google-cloud-run criteo-dataset streamlit s-learner t-learner treatment-effect ft-transformer champion-challenger qini-auc scikit-uplift

Updated May 4, 2026
Python

Improve this page

Add a description, image, and links to the scikit-uplift topic page so that developers can more easily learn about it.

Curate this topic

Add this topic to your repo

To associate your repository with the scikit-uplift topic, visit your repo's landing page and select "manage topics."

Learn more

Provide feedback

Saved searches

Use saved searches to filter your results more quickly