causal-machine-learning/kdd2021-tutorial
EconML/CausalML KDD 2021 Tutorial
This project helps data scientists, economists, and statisticians understand the true impact of their actions or interventions, rather than just correlations. It takes in observational data or results from A/B tests and outputs precise measurements of how different factors causally influence outcomes. You would use this if you need to make optimal policy decisions or targeted interventions with confidence.
168 stars. No commits in the last 6 months.
Use this if you need to rigorously measure the causal effect of a treatment or intervention (like a marketing campaign, new feature, or pricing change) and design optimal strategies based on those insights.
Not ideal if you are solely interested in predicting outcomes without understanding the underlying cause-and-effect relationships or if you lack basic knowledge in statistics, machine learning, and Python.
Stars
168
Forks
34
Language
Jupyter Notebook
License
Apache-2.0
Category
Last pushed
Aug 23, 2023
Commits (30d)
0
Get this data via API
curl "https://pt-edge.onrender.com/api/v1/quality/ml-frameworks/causal-machine-learning/kdd2021-tutorial"
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