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.
pip install dowhy
pip install econml
pip install causal-learn
An introduction to DoWhy, a Python library for causal inference that supports explicit modeling and testing of causal assumptions.
An introduction to EconML, a project under Microsoft ALICE team effort to direct Artificial Intelligence towards economic decision making.
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 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.
An end-to-end causal machine learning application in the Databricks ecosystem.
EconML’s Doubly Robust Learner model jointly estimates the effects of multiple discrete treatments.
EconML’s DML estimator uses price variations in existing data, estimates individualized responses to incentives.
EconML’s DRIV estimator uses this experimental nudge to interpret experiments with imperfect compliance
Causal Inference and Machine Learning in Practice.
Causal inference at scale presented at NABE.
Finding causal effects helps us learn about various phenomena in science and technology.
New features go beyond conventional effect estimation by attributing events to individual components of complex systems.