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
An introduction to DoWhy, a Python library for causal inference that supports explicit modeling and testing of causal assumptions.
Beyond predictive models: The causal story behind hotel booking cancellations.
Estimating the effect of a member rewards program.
Introducing the do-sampler for causal inference.
Understanding the question of why.
Causal inference at scale presented at NABE.
Identifying causal effects is an integral part of scientific inquiry. It helps us understand everything from educational outcomes to the effects of social policies to risk factors for diseases. Questions of cause-and-effect are also critical for the design and data-driven evaluation of many technological systems we build today.
New features go beyond conventional effect estimation by attributing events to individual components of complex systems.
For decades, causal inference methods have found wide applicability in the social and biomedical sciences. As computing systems start intervening in our work and daily lives, questions of cause-and-effect are gaining importance in computer science as well. To enable widespread use of causal inference, we are pleased to announce a new software library, DoWhy.