PyWhy's repositories
EconML
ALICE (Automated Learning and Intelligence for Causation and Economics) is a Microsoft Research project aimed at applying Artificial Intelligence concepts to economic decision making. One of its goals is to build a toolkit that combines state-of-the-art machine learning techniques with econometrics in order to bring automation to complex causal inference problems. To date, the ALICE Python SDK (econml) implements orthogonal machine learning algorithms such as the double machine learning work of Chernozhukov et al. This toolkit is designed to measure the causal effect of some treatment variable(s) t on an outcome variable y, controlling for a set of features x.
causal-learn
Causal Discovery in Python. It also includes (conditional) independence tests and score functions.
causaltune
AutoML for causal inference.
dodiscover
[Experimental] Global causal discovery algorithms
pywhy-graphs
[Experimental] Causal graphs that are networkx-compliant for the py-why ecosystem.
pywhy-stats
Python package for (conditional) independence testing and statistical functions related to causality.
py-why.github.io
Contains the code for https://py-why.github.io/
pywhy-notes
Keep track of discussions and meeting minutes.
governance
This repository describes the governance model for the PyWhy org