Zhuang-Zhuang-Liu/DeepUplift
Heterogeneous Treatment Effect Explorer
This project helps marketers, product managers, and data scientists understand the unique impact of a specific intervention (like an ad campaign or a new feature) on different customer segments. It takes customer data including who received a treatment and their outcomes, and outputs predictions on who is most likely to respond positively. This allows for more targeted strategies and efficient resource allocation.
No commits in the last 6 months.
Use this if you need to predict which individual customers will respond best to a particular marketing campaign or product change, helping you optimize your outreach efforts.
Not ideal if you're looking for a general-purpose machine learning library or if your primary goal is simple A/B testing rather than understanding heterogeneous treatment effects.
Stars
19
Forks
1
Language
Jupyter Notebook
License
—
Category
Last pushed
Aug 17, 2025
Commits (30d)
0
Get this data via API
curl "https://pt-edge.onrender.com/api/v1/quality/ml-frameworks/Zhuang-Zhuang-Liu/DeepUplift"
Open to everyone — 100 requests/day, no key needed. Get a free key for 1,000/day.
Higher-rated alternatives
py-why/dowhy
DoWhy is a Python library for causal inference that supports explicit modeling and testing of...
py-why/EconML
ALICE (Automated Learning and Intelligence for Causation and Economics) is a Microsoft Research...
uber/causalml
Uplift modeling and causal inference with machine learning algorithms
cdt15/lingam
Python package for causal discovery based on LiNGAM.
andrewtavis/causeinfer
Machine learning based causal inference/uplift in Python