Webinar Alert: Combining Bayes and Graph-based Causal Inference (29th Nov, 17:00 UTC)

[purna135]

Date: Wednesday, November 29, 2023
Time: 17:00 UTC / 9am PT / 12pm ET / 6pm Berlin
Speaker: Robert Ness, Researcher at Microsoft Research
Host: Dr. Thomas Wiecki, CEO & Founder of PyMC Labs
Register for the Zoom link:

Combining Bayes and Graph-based Causal Inference, Wed, Nov 29, 2023, 12:00 PM ...

Graphical causal inference and probabilistic programming share much history. For example, directed probabilistic graphical models were early versions of causal models, and d-separation (graphical criteria for conditional independence) provided the fundamentals for the do-calculus. Also, directed graphical models drove advancements in Bayesian inference algorithms and were the precursors of probabilistic programming languages like PyTorch.

Further, both causal models and probabilistic programming favor explicitly modeling the data-generating process. Yet, despite these commonalities, graphical causal inference and probabilistic programming have evolved into separate communities with little cross-talk beyond Bayesian inference of parameters in causal estimators. In this seminar, we discuss how to do causal graphical modeling with probabilistic programming, as well as tools and design patterns for doing so.

Resources

• Causal AI Book: Causal AI
• Related Paper: [2102.06626] Do-calculus enables estimation of causal effects in partially observed biomolecular pathways
• Probabilistic Machine Learning Workshop: Probabilistic Machine Learning | AltDeep School of AI
• Causal Modeling in Machine Learning Workshop: Causal Modeling in Machine Learning Workshop | AltDeep School of AI

Outline of Talk / Agenda:

• 5 min: Intro to PyMC Labs and speakers
• 45 min: Presentation, panel discussion
• 10 min: Q&A

About the speaker:

1. Robert Ness

   Researcher at Microsoft Research, where he focuses on causal reasoning, deep probabilistic modeling, language models and programming languages. He is author of the book Causal AI, and founder of AI learning platform Altdeep.ai. He has worked as a research engineer and received his Ph.D. in statistics from Purdue University. He is a Johns Hopkins SAIS alumnus.

   Connect with Robert Ness:
   LinkedIn: https://www.linkedin.com/in/osazuwa/
   Twitter: https://twitter.com/osazuwa
   GitHub: GitHub - altdeep/causalML: The open source repository for the Causal Modeling in Machine Learning Workshop at Altdeep.ai @ www.altdeep.ai/courses/causalML
   MSR: https://www.microsoft.com/en-us/research/people/robertness/

About the Host:

2. Dr. Thomas Wiecki (PyMC Labs)

   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.

   Connect with Thomas Wiecki:
   Website: https://www.pymc-labs.com/
   GitHub: twiecki (Thomas Wiecki) · GitHub
   Twitter: https://twitter.com/twiecki
   Blog posts: https://twiecki.io/

Code of Conduct:
Please note that participants are expected to abide by PyMC’s Code of Conduct.

[purna135]

In case you missed the live webinar, the recording has been posted to YouTube. Catch the recording to learn the practical aspects of “Combining Bayes and Graph-Based Causal Inference”:

Combining Bayes and Graph-based Causal Inference with Robert Ness

In this webinar, Robert Ness shares insights into merging Bayes and graph-based causal inference and discusses the application of probabilistic programming in building models, using examples from online gaming scenarios and in-game purchases.

You will learn about probabilistic modeling and understand the role of causal reasoning in gaming contexts.

Check out the additional resources in the video description, and stay up-to-date with the latest talks and updates by subscribing to our channel.