Thomas Wiecki's Guide To Causal Inference Using PyMC Ep 1 | CausalBanditsPodcast.com

Join us for an in-depth discussion on causality and its integration with Bayesian methods. Our special guest, Thomas Wiecki, a renowned Bayesian modeling expert and co-author of PyMC, will share his insights on how PyMC is advancing causal inference in machine learning and discuss the practical implications for data scientists. Join now to learn more about causality, causal AI, and machine learning and get the latest updates: causalbanditsp....
This discussion will illuminate the convergence of causal AI and Bayesian modeling, providing a unique perspective on the future of AI research. For more details on the cutting edge of machine learning and causality, subscribe to our channel.
Audio version available on the best podcast platforms: causalbanditsp...
✅ About The Guest
Thomas Wiecki, Ph.D., 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: / twiecki
Thomas Wiecki on Twitter/X: / twiecki
✅ About The Host
Aleksander (Alex) Molak is an independent machine learning researcher, educator, entrepreneur, and best-selling author in causality.
The Causal Book: amzn.to/3QhsRz4
✅ Links
PyMC's do-operator [Blog] (www.pymc-labs....)
PyMC webapge (www.pymc.io/we...)
Causal Bandits Team
Project Coordinator: Taiba Malik / taibasplay
Video and Audio Editing: Navneet Sharma, Aleksander Molak
This video is about Thomas Wiecki's Guide To Causal Inference Using PyMC Ep 1. But It also covers the following topics:
Causal Modeling Techniques
PyMC And Causal AI
Understanding PyMC
Video Title: Thomas Wiecki's Guide To Causal Inference Using PyMC Ep 1 | CausalBanditsPodcast.com
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#thomaswiecki #pymc #causalai #bayesianmodeling #machinelearning #causalinference
⚠️ Disclaimer: I do not accept any liability for any loss or damage incurred from you acting or not acting as a result of watching any of my publications. You acknowledge that you use the information I provide at your own risk. Do your research.
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©Causal Python with Alex Molak

[@CausalPython]

❗Should we build a Causal Experts Network to connect you with other like-minded people in causality? ❗Share your thoughts in the survey: bit.ly/3RM8ziz

[@NikTuzov]

Thank you 👍

[@pascalbercker7487]

Will you be doing anything on Bayesian networks (and would you review some Bayesian network software)? Does PyMC include graphical capabilities? What are the pros and cons between just probabilistic programming vs. Bayesian networks? I have the impression that probabilistic programming has more friction for newcomers (like me) to causal modeling without something graphical to look at.

[@CausalPython]

Hi Pascal, Great questions. The choice of your framework really depends on what types of problems you're trying to solve. If you're just starting with causal inference, perhaps a package like DoWhy would be a good place for you to start, as it offers a very intuitive and well-structured process. If you're looking for a relatively simple tool for Bayesian networks and causal Bayesian networks, PyAgrum might be a good choice. If you want something more versatile, with approximate inference and more advanced options so on, then PyMC, and Pyro/Chirho will be a good choice. Finally, if you're primarily interested in causal identification over graphical structures, you might find GRAPL-causal or Ananke useful. Does this answer your questions?

[@awadelrahman]

Loved this episode, great questions and super insightful answers! Thanks for keeping it interesting. Keep it up !❤

[@CausalPython]

Thank you for the feedback, @awadelrahman - appreciate it!

[@aki-kakko]

Great discussion!

[@local_network]

Great video. Love the content.

[@galenseilis5971]

I am looking forward to these podcasts being available on apps such as Audible so I can listen on the go. I think that causal modelling is fascinating I would love to learn more from this podcast.

[@CausalPython]

Thanks for sharing @galenseilis5971 We'll be launching audio only version on all major podcast platforms next week.

[@CausalPython]

Here are currently available audio platforms: causalbanditspodcast.buzzsprout.com

[@musiknation7218]

How can I contact him for help in my Bayesian modelling of my research

[@CausalPython]

See the links in the video description. There's a section *About the guest* there and you'll find the links to contact Thomas.

[@dedajma]

ooooooooooooooooooooooooooooh yeah !

[@musiknation7218]

I need a Bayesian modelling software

[@CausalPython]

You can use PyMC for Bayesian modeling (www.pymc.io/welcome.html)

[@pascalbercker7487]

I use Netica - for Bayesian Networks (from Norsys). There are others like AgenaRisk, Bayesialab, Genie (some free, some expensive).

[@user-wr4yl7tx3w]

why are the earphones necessary, given the proximity?

[@CausalPython]

Good question. What makes you think they are necessary?