❗ uplift modeling in scikit-learn style in python 🐍
Collaborative project for documenting ML/DS learnings.
A repo with functions for building various COMs and GCOMs quickly.
Decision-grade B2B uplift targeting policy that captures revenue while eliminating Admin-level churn risk.
HTE uplift modelling for retention — CausalForestDML, Qini/AUUC, PolicyTree, ENBP constraint, Consumer Duty fairness audit
Customer analytics snapshot covering churn, segmentation, retention, uplift, and model evaluation.
Causal rule discovery in procurement logs (IntRob’26 paper)
Uplift modelling using meta learners
Causal inference & uplift modeling on a randomised e-mail experiment — A/B analysis, S/T-learners, Qini curves, and a profit-optimal targeting policy (Python · LightGBM)
Nudge finds customers who convert because of a promotion, not just customers who convert. Built with econml, DuckDB, XGBoost, and Streamlit.
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