❗ 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.
Customer analytics snapshot covering churn, segmentation, retention, uplift, and model evaluation.
Decision-grade B2B uplift targeting policy that captures revenue while eliminating Admin-level churn risk.
Causal rule discovery in procurement logs (IntRob’26 paper)
HTE uplift modelling for retention — CausalForestDML, Qini/AUUC, PolicyTree, ENBP constraint, Consumer Duty fairness audit
Uplift modelling benchmarked against a synthetic oracle where true individual treatment effects are known. S/T/X-learners, Qini evaluation, Hillstrom.
Uplift modelling using meta learners
Nudge finds customers who convert because of a promotion, not just customers who convert. Built with econml, DuckDB, XGBoost, and Streamlit.
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)
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