TimeLovercc/CAF-GNN
[CIKM 2023] Towards Fair Graph Neural Networks via Graph Counterfactual.
This project helps machine learning practitioners identify and reduce bias in classification decisions made by graph neural networks. It takes your existing graph-structured data and trained models, and outputs a refined model that makes fairer predictions. Anyone working with sensitive data like credit applications or loan approvals, where fairness and preventing discrimination are critical, would find this useful.
No commits in the last 6 months.
Use this if you need to ensure that your graph neural network models make fair, unbiased predictions on sensitive data, such as credit scores or bail risk, and you are comfortable with Python and PyTorch.
Not ideal if you are looking for a plug-and-play solution without any coding or if you are not familiar with machine learning development environments.
Stars
14
Forks
5
Language
Python
License
MIT
Category
Last pushed
Mar 04, 2025
Commits (30d)
0
Get this data via API
curl "https://pt-edge.onrender.com/api/v1/quality/ml-frameworks/TimeLovercc/CAF-GNN"
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