Welcome to the 10th event of the PyMCon Web Series! As part of this series, most events will have both an asynchronous component and a live Q&A.
Speaker: Thomas Wiecki, CEO & founder of PyMC Labs.
Event type: Recorded Talk with Live Q&A
Q&A Date/Time: 2023-09-28T13:00:00Z (subscribe here for email updates)
Register for Q&A: Meetup event (to get the Zoom link)
Website: PyMCon Events · PyMCon Web Series
NOTE: This session is exclusively for Q&A. We kindly request that you watch the recording before joining the event. Plus, The event will be recorded. Subscribe to the PyMC YouTube channel for notifications.
Causal analysis is rapidly gaining popularity, but why? Machine learning methods might help us predict what’s going to happen with great accuracy, but what’s the value of that if it doesn’t tell us what to do to achieve a desirable outcome? Without a causal understanding of the world, it’s often impossible to identify which actions lead to a desired outcome.
Causal analysis is often embedded in a frequentist framework, which comes with some well-documented baggage. In this talk, Thomas will present how we can super-charge PyMC for Bayesian Causal Analysis by using a powerful new feature: the do operator.
Dr. Thomas Wiecki
Dr. Thomas Wiecki is an author of PyMC, the leading platform for statistical data science. To help businesses solve some of their trickiest data science problems, he assembled a world-class team of Bayesian modelers and founded PyMC Labs – the Bayesian consultancy. He did his PhD at Brown University, studying cognitive neuroscience.
Adia Lab is an independent, Abu Dhabi-based laboratory dedicated to basic and applied research in data and computational sciences.
ADIA Lab focuses on societally-important topics such as climate change and energy transition, blockchain technology, financial inclusion and investing, decision making, automation, cybersecurity, health sciences, education, telecommunications, and space, by conducting cutting-edge research in Data Science, Artificial Intelligence, Machine Learning, and High-Performance Computing.