Causal Bandits Podcast

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Video version available on YouTube
Recorded on Sep 27, 2023 in München, Germany
From supply chain to large language models and back
Ishansh realized the potential of data when he was just 10 years old, during his time as a junior cricket player.
His journey led him to ask questions about the mechanisms behind the observed events.
Can large language models (LLMs) help in building an industrial causal graph?
What inspires stakeholders to share their knowledge and which causal discovery algorithms have been most effective for Ishansh's supply chain use case?
Hear the insights from one of the BMW Group's fastest-rising young data science talents.
Ready?
About The Guest
Ishansh Gupta is a Lead Data Scientist at BMW Group. Previously, he worked for several companies, including a legendary German sports club SV Werder Bremen. He studied Computer Science, and co-founded an educational startup during his study years. He has supervised or supported students in various universities, including the Munich-based TUM and MIT.
Connect with Ishansh:
- Ishansh on Twitter/X
- Ishansh on LinkedIn
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
Papers
Full list of papers here
Books
- Molak (2023) - Causal Inference and Discovery in Python
- Pearl & Mackenzie (2019) - The Book of Why
Other
- causaLens

Causal Bandits Team
Project Coordinator: Taiba Malik
Video and Audio Editing: Navneet Sharma, Aleksander

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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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Recorded on Jan 17, 2024 in London, UK.
Video version available here
What makes so many predictions about the future of AI wrong?
And what's possible with the current paradigm?
From medical imaging to song recommendations, the association-based paradigm of learning can be helpful, but is not sufficient to answer our most interesting questions.
Meet Athanasios (Thanos) Vlontzos who looks for inspirations everywhere around him to build causal machine learning and causal inference systems at Spotify's Advanced Causal Inference Lab.
In the episode we discuss:
- Why is causal discovery a better riddle than causal inference?
- Will radiologists be replaced by AI in 2024 or 2025?
- What are causal AI skeptics missing?
- Can causality emerge in Euclidean latent space?
Ready to dive in?
About The Guest
Athanasios (Thanos) Vlontzos, PhD is a Research Scientist at Advanced Causal Inference Lab at Spotify. Previousl;y, he worked at Apple, at SETI Institute with NASA stakeholders and published in some of the best scientific journals, including Nature Machine Learning. He's specialized in causal modeling, causal inferernce, causal discovery and medical imaging.
Connect with Athanasios:
- Athanasios on Twitter/X
- Athanasios on LinkedIn
- Athanasios's web page
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
The full list of links can be found 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

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Causal AI: The Melting Pot. Can Physics, Math & Biology Help Us?
What is the relationship between physics and causal models?
What can science of non-human animal behavior teach causal AI researchers?
Bernhard Schölkopf's rich background and experience allow him to combine perspectives from computation, physics, mathematics, biology, theory of evolution, psychology and ethology to build a deep understanding of underlying principles that govern complex systems and intelligent behavior.
His pioneering work in causal machine learning has revolutionized the field, providing new insights that enhance our ability to understand causal relationships and mechanisms in both natural and artificial systems.
In the episode we discuss:

• Does evolution favor causal inference over correlation-based learning?
• Can differential equations help us generalize structural causal models?
• What new book is Bernhard working on?
• Can ethology inspire causal AI researchers?

Ready to dive in?
About The Guest
Bernhard Schölkopf, PhD is a Director at Max Planck Institute for Intelligent Systems. He's one of the cofounders of European Lab for Learning & Intelligent Systems (ELLIS) and a recepient of the ACM Allen Newell Award, BBVA Foundation Frontiers of Knowledge Award, and more. His contributions to modern machine learning are hard to overestimate. He's a an affiliated professor at ETH Zürich, honorary professor at the University of Tübingen and the Technical University Berlin. His pioneering work on causal inference and causal machine learning inspired thousands of researchers and practitioners worldwide.
Connect with Bernhard:

• Bernhard on Twitter/X
• Bernhard on LinkedIn
• Bernhard's web page

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

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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Video version of this episode is available here
Causal Inference with LLMs and Reinforcement Learning Agents?
Do LLMs have a world model?
Can they reason causally?
What's the connection between LLMs, reinforcement learning, and causality?
Andrew Lampinen, PhD (Google DeepMind) shares the insights from his research on LLMs, reinforcement learning, causal inference and generalizable agents.
We also discuss the nature of intelligence, rationality and how they play with evolutionary fitness.
Join us in the journey!
Recorded on Dec 1, 2023 in London, UK.
About The Guest
Andrew Lampinen, PhD is a Senior Research Scientist at Google DeepMind. He holds a PhD in PhD in Cognitive Psychology from Stanford University. He's interested in cognitive flexibility and generalization, and how these abilities are enabled by factors like language, memory, and embodiment.
Connect with Andrew:
- Andrew on Twitter/X
- Andrew's web page
About The Host
Aleksander (Alex) Molak is an independent machine learning researcher, educator, entrepreneur and a best-selling author in the area of causality (https://amzn.to/3QhsRz4).
Connect with Alex:
- Alex on the Internet
Links
Papers
- Lampinen et al. (2023) - "Passive learning of active causal strategies in agents and language models" (https://arxiv.org/pdf/2305.16183.pdf)
- Dasgupta, Lampinen, et al. (2022) Language models show human-like content effects on reasoning tasks" (https://arxiv.org/abs/2207.07051)
- Santoro, Lampinen, et al. (2021) - "Symbolic behaviour in artificial intelligence" (https://www.researchgate.net/publication/349125191_Symbolic_Behaviour_in_Artificial_Intelligence)
- Webb et al. (2022) - “Emergent Analogical Reasoning in Large Language Models” (https://arxiv.org/abs/2212.09196)
Books
- Tomasello (2019) - “Becoming Human” (ht

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

Support the show
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