Scalable probabilistic impact modeling

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

A flexible python package for cost-aware uplift modelling.

Uplift modeling and causal inference with machine learning algorithms

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

Towards causality in neural networks.

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

Uplift modeling and evaluation library. Actively maintained pypi version.

Machine learning based causal inference/uplift in Python

❗ uplift modeling in scikit-learn style in python 🐍

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

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

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

https://arxiv.org/abs/2009.01561

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.

Causal Simulations for Uplift Modeling

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