Uplift modeling and causal inference with machine learning algorithms

❗ uplift modeling in scikit-learn style in python 🐍

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

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

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

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

Uplift modeling and evaluation library. Actively maintained pypi version.

🛠 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】

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

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

Machine learning based causal inference/uplift in Python

https://arxiv.org/abs/2009.01561

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

A flexible python package for cost-aware uplift modelling.

Lightweight uplift modeling framework for Python

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

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

A powerful tree-based uplift modeling system.

Scalable probabilistic impact modeling