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Video version of this episode is available on YouTube
Recorded on Aug 24, 2023 in Berlin, Germany


Does Causality Align with Bayesian Modeling?

Structural causal models share a conceptual similarity with the models used in probabilistic programming.

However, there are important theoretical differences between the two. Can we bridge them in practice?

In this episode, we explore Thomas' journey into causality and discuss how his experience in Bayesian modeling accelerated his understanding of basic causal concepts.

We delve into new causally-oriented developments in PyMC - an open-source Python probabilistic programming framework co-authored by Thomas - and discuss practical aspects of causal modeling drawing from Thomas' experience.

"It's great to be wrong, and this is how we learn" - says Thomas, emphasizing the gradual and iterative nature of his and his team's successful projects.

Further down the road, we take a look at the opportunities and challenges in uncertainty quantification, briefly discussing probabilistic programming, Bayesian deep learning and conformal prediction perspectives.

Lastly, Thomas shares his personal journey from studying computer science, bioinformatics, and neuroscience, to becoming a major open-source contributor and an independent entrepreneur.

Ready to dive in?


About The Guest
Thomas Wiecki, Phd is a co-author of PyMC - one of the most recognizable Python probabilistic programming frameworks - and the CEO of PyMC Labs. 
Connect with Thomas: 

• Thomas Wiecki on LinkedIn
• Thomas Wiecki on Twitter/X

About The Host
Aleksander (Alex) Molak is an independent machine learning researcher, educator, entrepreneur and a best-selling author in the area of causality.

Connect with Alex: 

• Alex on the Internet: https://bit.ly/aleksander-molak 

Links
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Causal Bandits Podcast
Causal AI || Causal Machine Learning || Causal Inference & Discovery
Web: https://causalbanditspodcast.com

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Join Causal Python Weekly: https://causalpython.io
The Causal Book: https://amzn.to/3QhsRz4