Uplift modeling and causal inference with machine learning algorithms

:exclamation: uplift modeling in scikit-learn style in python :snake:

YLearn, a pun of "learn why", is a python package for causal inference

CausalLift: Python package for causality-based Uplift Modeling in real-world business

This contains projects based on Algorithmic Marketing like Marketing Mix Modeling, Attribution Modeling & Budget Optimization, RFM Analysis, Customer Segmentation, Recommendation Systems, and Social Media Analytics

因果推理&AB实验相关论文小书库

train models in pytorch, Learn to Rank, Collaborative Filter, Heterogeneous Treatment Effect, Uplift Modeling, etc

🛠 How to Apply Causal ML to Real Scene Modeling？How to learn Causal ML？【✔从Causal ML到实际场景的Uplift建模】

Implementation of paper DESCN, which is accepted in SIGKDD 2022.

Uplift modeling and evaluation library. Actively maintained pypi version.

Machine learning based causal inference/uplift in Python

Algorithmic Marketing based Project to do Customer Segmentation using RFM Modeling and targeted Recommendations based on each segment

A flexible python package for cost-aware uplift modelling.

End-to-end uplift modeling pipeline for customer retention using T-Learner and X-Learner with XGBoost. Includes AUUC/Qini evaluation, SHAP interpretability, and business profit optimization.

A powerful tree-based uplift modeling system.

My collection of causal inference algorithms built on top of accessible, simple, out-of-the-box ML methods, aimed at being explainable and useful in the business context

Lightweight uplift modeling framework for Python

https://arxiv.org/abs/2009.01561

A Python Framework for Automatically Evaluating various Uplift Modeling Algorithms to Estimate Individual Treatment Effects

Heterogeneous Treatment Effect Explorer

This repository consists of predicting dynamic pricing, churn predictions using sales and marketing data for understanding users' behaviour.

Causal Simulations for Uplift Modeling

Scalable probabilistic impact modeling

DSND Term 2 Portfolio Exercise: Optimize promotion offers for Starbucks

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.

Customer targeting model to optimize promotion targeting, on simulated data from Starbucks. (work in progress)

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

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.

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