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Video version available on YouTube
Recorded on Aug 25, 2023 in Berlin, Germany


Is Marketing Intrinsically Causal?

After spending 5 years talking to mathematicians, Juan decided to look for new opportunities that would offer him more immediate impact on the world.

Little did he know that this journey will lead him to become a Senior Data Scientist at Wolt - one of the global food delivery leaders with operations in 25 countries.

In this episode we discuss Juan's journey towards data science, how causality was close to his heart from the very beginning and why starting simple is a good thing.

Juan shares how his background in physics and advanced geometry helps him tackle causal problems he faces daily in his work in the fields of marketing and pricing.

"It's fundamental for decision-making" - he says when asked about the future of causal modeling and causal AI.

We discuss the consequences of ignoring the causal structure in marketing problems.

Finally, Juan shares how inaccurate world models contributed to a distaste for wearing gloves by someone dear to him.

Ready to dive in?


About The Guest
Juan Orduz, Phd is a Senior Data Scientist at Wolt. He is a blogger and an open source contributor. Juan holds a PhD in geometric analysis.
Connect with Juan:
- Juan on LinkedIn
- Juan on Twitter/X
- Juan's Blog 

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

Links (see here for the full list)

Causal Bandits Team
Project Coordinator: Taiba Malik
Video Editors: Navneet S.

Support the show

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

Connect on LinkedIn: https://www.linkedin.com/in/aleksandermolak/
Join Causal Python Weekly: https://causalpython.io
The Causal Book: https://amzn.to/3QhsRz4

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`from causality import solution`
Recorded on Sep 04, 2023 in London, United Kingdom


A Python package that would allow us to address an arbitrary causal problem with a one-liner does not yet exist.

Fortunately, there are other ways to implement and deploy causal solutions at scale.

In this episode, Andrew shares his journey into causality and gives us a glimpse into the behind-the-scenes of his everyday work at causaLens.

We discuss new ideas that Andrew and his team use to enhance the capabilities of available open-source causal packages, how they strive to build and maintain a highly modularized and open platform.

Finally, we talk about the importance of team work and what Andrew's parents did to make him feel nurtured & supported.

Ready?

About The Guest
Andrew Lawrence is the Director of Research at causaLens (https://causalens.com/) Connect with Andrew: 

• Andrew on LinkedIn: https://www.linkedin.com/in/andrew-r-lawrence/ 

About The Host
Aleksander (Alex) Molak is an independent ML 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

• Code and Blogs
  • DARA open-source framework (https://bit.ly/3Ql1VhF) 
  • causaLens GitHub (https://bit.ly/3QmoUJz) 
  • causaLens Blog (https://bit.ly/46TieJF) 
• Videos
  • Brady Neal Introduction to Causality
• Books
  • Bishop (2006) - Pattern Recognition and Machine Learning 
  • Molak (2023) - Causal Inference and Discovery in Python
  • Pearl & Mackenzie (2019) - The Book of Why 

Support the show

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

Connect on LinkedIn: https://www.linkedin.com/in/aleksandermolak/
Join Causal Python Weekly: https://causalpython.io
The Causal Book: https://amzn.to/3QhsRz4

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Support the show

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
Full list of link

Support the show

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

Connect on LinkedIn: https://www.linkedin.com/in/aleksandermolak/
Join Causal Python Weekly: https://causalpython.io
The Causal Book: https://amzn.to/3QhsRz4

Send us a text

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Video version of this episode available on YouTube
Recorded on Aug 14, 2023 in Frankfurt, Germany


Are Large Language Models (LLMs) causal?

Some researchers have shown that advanced models like GPT-4 can perform very well on certain causal benchmarks.

At the same time, from the theoretical point of view it's highly unlikely that these models can learn causal structures. Is it possible that large language models are not causal, but talk causality?

In our conversation we explore this question from the point of view of the formalism proposed by Matej and his colleagues in their "Causal Parrots" paper.

 We also discuss Matej's journey from the dream of becoming a hacker to a successful AI and then causality researcher. Ready to dive in?

Links 

• Events
  • Causality Discussion Group (https://discuss.causality.link/) 
  • Eastern European Machine Learning Summer School (https://www.eeml.eu/home) 
• Videos 
  • Prof. Moritz Helmstaedter on connectomics
• Books 
  • Molak (2023) - Causal Inference & Discovery in Python 
  • Pearl & Mackenzie (2019) - The Book of Why
• Papers 
  • For full list of papers see the episode's description here.

Support the show

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

Connect on LinkedIn: https://www.linkedin.com/in/aleksandermolak/
Join Causal Python Weekly: https://causalpython.io
The Causal Book: https://amzn.to/3QhsRz4