#8888th of 126 in ML frameworks and libraries
causalml
Python library for uplift modeling and causal inference with ML estimators
- Stars
- 6k
- License
- custom license
- Last commit
- Oct 2026
- Last release
- Oct 2026
- Language
- Python
5,763 on 3 Apr 20266,037 on 30 Oct 2026
From GitHub's daily star counts.
Checked on GitHub.
Overview
Causal ML is a Python package that estimates the Conditional Average Treatment Effect (CATE) of an intervention on an outcome, from experimental or observational data, through a common estimator interface. It implements uplift trees, meta-learners, doubly robust and neural-network methods from the cited literature. It also ships loaders for the LaLonde, IHDP and Twins benchmarks, with PEHE, ATE error and policy risk metrics.
Who it is for: Data scientists running uplift and A/B treatment-effect analysis in Python
Strengths
- Common interface across uplift trees, meta-learners, doubly robust and neural CATE estimators
- Benchmark loaders (LaLonde, IHDP, Twins) with SHA256-verified downloads and ground-truth metrics
- Docs include an estimator decision guide, tutorial, quickstart and example notebooks
- Published on PyPI with an API reference and changelog
Weaknesses
- README warns some experimental code has APIs subject to change
- Tabular causal estimation only; no LLM or generative model features
- Install steps are not in the README, only linked to the docs
- GitHub detects license as NOASSERTION, though README states Apache 2.0
What it needs
- Models: uplift trees, meta-learners, doubly robust estimators, neural network CATE estimators
Quick answers
Is causalml open source?
Its code is public on GitHub under its own license terms; read them before you use it at work.
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