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

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.

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

Causalis - State-of-the-art robust causal inference for experiments and observational data in python