Why PyWhy?
PyWhy’s mission is to build an open-source ecosystem for causal machine learning that moves forward the state-of-the-art and makes it available to practitioners and researchers. We build and host interoperable libraries, tools, and other resources spanning a variety of causal tasks and applications, connected through a common API on foundational causal operations and a focus on the end-to-end analysis process.
Install
pip install dowhy
pip install econml
pip install causal-learn
Learn
DoWhy | An end-to-end library for causal inference
An introduction to DoWhy, a Python library for causal inference that supports explicit modeling and testing of causal assumptions.
EconML | Automated Learning and Intelligence for Causation and Economics
An introduction to EconML, a project under Microsoft ALICE team effort to direct Artificial Intelligence towards economic decision making.
EconML | General Tutorial on Causal Inference
If you are new to causal inference, it may be helpful to walk through a quick overview of concepts and techniques that we refer to over the course of the documentation. We provide a high level introduction to causal inference tailored for EconML.
causal-learn | Causal Discovery for Python
causal-learn is a Python translation and extension of the Tetrad java code. It offers the implementations of up-to-date causal discovery methods as well as simple and intuitive APIs.
Case Studies
Improving business metrics for better impact using the CausalTune library
This tutorial provides an introduction to improving business metrics using the ERUPT metric and the CausalTune library in Python. It shows a practical example and the use of the ERUPT metric for optimizing clickthrough rates.
Intro to Causal AI Using the DoWhy Library in Python
This tutorial provides an introduction to causal AI using the DoWhy library in Python. It discusses fundamental principles and offers code examples.
Using PyWhy in Databricks
An end-to-end causal machine learning application in the Databricks ecosystem.
Multi-investment Attribution
EconML’s Doubly Robust Learner model jointly estimates the effects of multiple discrete treatments.
News
We're co-organizing a KDD workshop on causal inference and machine learning
Causal Inference and Machine Learning in Practice.
National Association for Business Economics Conference
Causal inference at scale presented at NABE.
DoWhy evolves to independent PyWhy model to help causal inference grow
Finding causal effects helps us learn about various phenomena in science and technology.
AWS contributes novel causal machine learning algorithms to DoWhy Python library
New features go beyond conventional effect estimation by attributing events to individual components of complex systems.