Uplift modeling and causal inference with machine learning algorithms
-
Updated
Jul 5, 2024 - Python
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
Uplift modeling and evaluation library. Actively maintained pypi version.
train models in pytorch, Learn to Rank, Collaborative Filter, Heterogeneous Treatment Effect, Uplift Modeling, etc
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.
🛠 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】
Machine learning based causal inference/uplift in Python
This repository consists of predicting dynamic pricing, churn predictions using sales and marketing data for understanding users' behaviour.
Lightweight uplift modeling framework for Python
A Python Framework for Automatically Evaluating various Uplift Modeling Algorithms to Estimate Individual Treatment Effects
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 powerful tree-based uplift modeling system.
study about causality
DSND Term 2 Portfolio Exercise: Optimize promotion offers for Starbucks
Add a description, image, and links to the uplift-modeling topic page so that developers can more easily learn about it.
To associate your repository with the uplift-modeling topic, visit your repo's landing page and select "manage topics."