What it does
Causal ML uses machine learning to answer cause-and-effect questions. Instead of just spotting patterns, it estimates the real impact of an action, such as sending an offer, on an outcome like a sale, and how that impact differs from person to person.
The README gives examples like choosing which customers to target with an ad campaign, and picking the best way to contact each customer. This approach is often called uplift modeling.
It includes several estimation methods, guidance on choosing between them, and standard test datasets.
Who it's for
Data scientists and analysts who run experiments or marketing campaigns and need to measure real effects.
Can I use it without coding?
Developers only. It is a Python library used in code. New to this? Start here.
Can I run it?
- Cost
- FreeNothing to pay or sign up for.
- Computer
- An ordinary computer is fine
- Privacy
- Runs on your own device: your data stays with you
- API key
- Not needed
Try it
Using Claude Code, Cursor or another AI coding assistant? Paste the prompt and it will walk you through the setup.New to this?
Before you install
- Only download from the official GitHub page. Copies with similar names exist for many popular projects.
- No standard license listed. Check the GitHub page before using it for business.
- More on staying safe
Where people are talking about it
- Feature Selection Methods for Uplift ModelingResearch paper · May 2020
- CausalML: Python package for causal machine learningResearch paper · Feb 2020