smote meets uplift modeling

Voucher Campaign Optimization Project

Advanced Microeconomics final project for DSE

Research on the impacts of algorithmic collective action on personalized user marketing

Towards causality in neural networks.

A modified uplift modeling technique to convert "treatment nonresponders" to "responders" is proposed through multifaceted interventions in market campaigns.

A complete end-to-end AI experimentation & causal inference project using A/B testing, X-Learner, CATE estimation, and uplift segmentation on 1.5M+ synthetic SaaS behavioral records. Includes statistical analysis, causal ML workflow, uplift modeling, feature importance, and business-ready insights for AI feature rollout & monetization.

Statistical analysis to see effectiveness of email marketing campaign. Used regression, DoWhy & CausalML to calculate treatment effects. Feature importance & CATE, ITEs.

This is a project by Asmir Muminovic and Lukas Kolbe, which was created for the Applied Predictive Analytics class held by the Chair of Information Systems at the Humboldt University of Berlin

Uplift Modeling to identify the pursuable group of users from all the users in order to send them encouragement (in terms of coupons or other offers) to buy the product more without spending resources to convert those users who are not willing or interested to buy the product even after encouragement.

Marketing Analytics project : Promotion email targeting with uplift and causal forest model

Medicine intake assessment in patients with uplift modelling

Causal Simulations for Uplift Modeling

This repository provides a platform for the predicting of future stock prices based on historical stock prices. Time series analysis is extensively explored in this project. The repository also contains pipelines that can be reused for analyzing and predicting stock prices and feature extraction.

An ensemble is based on the notion of combining models. While uplift modeling combines supervised modeling with A-B testing, which is a simple type of randomized experiment.

This repository houses the implementation and analysis of an uplift modeling approach aimed at optimizing marketing promotion campaigns.

Weighted doubly robust learning for uplift modeling

📊 Analyze customer segments using RFM modeling to boost retention and reduce churn with actionable insights and an interactive BI dashboard.

Fast multiple choice knapsack, optimised for settings with unequal patient treatment eligibilities