PyWhy

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

dowhy

DoWhy is a Python library for causal inference that supports explicit modeling and testing of causal assumptions. DoWhy is based on a unified language for causal inference, combining causal graphical models and potential outcomes frameworks.

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.

py-why.github.io

Contains the code for https://py-why.github.io/

pywhy-graphs

[Experimental] Causal graphs that are networkx-compliant for the py-why ecosystem.

pywhy-llm

Experimental library integrating LLM capabilities to support causal analyses

pywhy-notes

Keep track of discussions and meeting minutes.

pywhy-stats

Python package for (conditional) independence testing and statistical functions related to causality.

py-why/dowhy

DoWhy is a Python library for causal inference that supports explicit modeling and testing of causal assumptions. DoWhy is based on a unified language for causal inference, combining causal graphical models and potential outcomes frameworks.

py-why/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.

py-why/causal-learn

Causal Discovery in Python. It also includes (conditional) independence tests and score functions.

py-why/causaltune

AutoML for causal inference.

py-why/dodiscover

[Experimental] Global causal discovery algorithms

py-why/pywhy-llm

Experimental library integrating LLM capabilities to support causal analyses

py-why/pywhy-graphs

[Experimental] Causal graphs that are networkx-compliant for the py-why ecosystem.

py-why/pywhy-stats

Python package for (conditional) independence testing and statistical functions related to causality.

py-why/py-why.github.io

Contains the code for https://py-why.github.io/

py-why/pywhy-notes

Keep track of discussions and meeting minutes.

py-why/governance

This repository describes the governance model for the PyWhy org

py-why/graphs

[Not used] Now, an open PR for mixed-edge graph support is open in networkx

py-why/dowhy-example-notebooks-deps-dockerfile

py-why/.github