rpatrik96/nl-causal-representations
This is the code for the paper Jacobian-based Causal Discovery with Nonlinear ICA, demonstrating how identifiable representations (particularly, with Nonlinear ICA) can be used to extract the causal graph from an underlying structural equation model (SEM).
This project helps machine learning researchers and data scientists uncover the underlying 'cause and effect' relationships within complex datasets. You provide numerical data that reflects a system where some factors influence others, and it outputs a causal graph, showing which variables directly affect which others. This is for researchers working on advanced causal discovery methods.
No commits in the last 6 months.
Use this if you are a machine learning researcher or data scientist experimenting with cutting-edge techniques to identify causal links from observational data, particularly using nonlinear independent component analysis (ICA).
Not ideal if you are looking for a straightforward, off-the-shelf tool for causal inference in business intelligence or simpler applications, or if you are not comfortable working with research code and academic methodologies.
Stars
22
Forks
2
Language
Python
License
MIT
Category
Last pushed
Sep 05, 2024
Commits (30d)
0
Get this data via API
curl "https://pt-edge.onrender.com/api/v1/quality/ml-frameworks/rpatrik96/nl-causal-representations"
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