blind-contours/SuperNOVA
:dizzy: :dart: Automatic identification of variable and interaction importance using basis functions and non-parametric estimation of interactions/effect modification using joint stochastic interventions.
This project helps researchers and data analysts understand complex relationships between multiple factors and an outcome. You input your observational data, including exposures, covariates, mediators, and an outcome, and it outputs clear, structured tables that show how these factors interact, modify effects, or mediate outcomes. It's designed for anyone needing to identify and quantify causal effects in real-world scenarios.
No commits in the last 6 months.
Use this if you need to analyze observational data to uncover non-parametric interactions, effect modifications, or mediation pathways among your variables.
Not ideal if your primary goal is simple predictive modeling without a focus on causal inference or understanding specific interaction mechanisms.
Stars
9
Forks
—
Language
R
License
—
Category
Last pushed
Oct 31, 2023
Commits (30d)
0
Get this data via API
curl "https://pt-edge.onrender.com/api/v1/quality/ml-frameworks/blind-contours/SuperNOVA"
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