bytedance/CausalMatch
CausalMatch is a Bytedance research project aimed at integrating cutting-edge machine learning and econometrics methods to bring about automation in decision-making process.
This tool helps data scientists and analysts evaluate the real-world impact of a program or intervention, like a new marketing campaign or policy change. You provide observational data (e.g., customer behavior, patient records) where some individuals received a 'treatment' and others didn't. The tool then intelligently matches similar individuals from both groups, allowing you to estimate the true average effect of the treatment, providing clearer insights than simple comparisons.
102 stars. Available on PyPI.
Use this if you need to determine the causal effect of an intervention or program when you can't run a perfect A/B test and only have observational data.
Not ideal if you are looking for a simple correlation analysis or if you can conduct a randomized controlled trial for your evaluation.
Stars
102
Forks
6
Language
Jupyter Notebook
License
Apache-2.0
Category
Last pushed
Dec 02, 2025
Commits (30d)
0
Dependencies
9
Get this data via API
curl "https://pt-edge.onrender.com/api/v1/quality/ml-frameworks/bytedance/CausalMatch"
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