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Description
Causal Bandits Podcast with Alex Molak is here to help you learn about causality, causal AI and causal machine learning through the genius of others.
The podcast focuses on causality from a number of different perspectives, finding common grounds between academia and industry, philosophy, theory and practice, and between different schools of thought, and traditions.
Your host, Alex Molak is an entrepreneur, independent researcher and a best-selling author, who decided to travel the world to record conversations with the most interesting minds in causality.
Enjoy and stay causal!
Keywords: Causal AI, Causal Machine Learning, Causality, Causal Inference, Causal Discovery, Machine Learning, AI, Artificial Intelligence
19 Episodes
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Send us a Text Message. 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 ...
Send us a Text Message. What makes two tech giants collaborate on an open source causal AI package?Emre's adventure with causal inference and causal AI has started before it was trendy. He's one of the original core developers of DoWhy - one of the most popular and powerful Python libraries for causal inference - and a researcher focused on the intersection of causal inference, causal discovery, generative modeling and social impact.His unique perspective, inspired by his experience with...
Send us a Text Message.Recorded on Jan 17, 2024 in London, UK. Video version available hereWhat 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 infer...
Send us a Text Message.Video version available here Are markets efficient, and if not, can causal models help us leverage the inefficiencies?Do we really need to understand what we're modeling?What's the role of symmetry in modeling financial markets?What are the main challenges in applying causal models in finance?Ready to dive in? About The GuestAlexander Denev is the CEO of Turnleaf Analytics. He's an author of multiple books on financial modeling and a former Head of AI (Financial Service...
Send us a Text Message.Love Causal Bandits Podcast?Help us bring more quality content: Support the showVideo version of this episode is available hereCausal 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 ...
Send us a Text Message.Support the showVideo version available on YouTubeDo We Need Probability?Causal inference lies at the very heart of the scientific method. Randomized controlled trials (RCTs; also known as randomized experiemnts or A/B tests) are often called "the golden standard for causal inference".It's a less known fact that randomized trials have their limitations in answering causal questions.What are the most common myths about randomization?What causal questions can and cannot b...
Send us a Text Message.Support the showVideo version available on YouTube Recorded on Nov 12, 2023 in Undisclosed location, Undisclosed locationFrom Systems Biology to CausalityRobert always loved statistics.He went to study systems biology, driven by his desire to model natural systems.His perspective on causal inference encompasses graphical models, Bayesian inference, reinforcement learning, generative AI and cognitive science.It allows him to think broadly about the problems we encounter ...
Send us a Text Message.Support the showVideo version available on YouTubeRecorded on Sep 27, 2023 in München, GermanyFrom supply chain to large language models and backIshansh 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 know...
Send us a Text Message.Support the showVideo version of this episode is available on YouTubeRecorded on Oct 15, 2023 in São Paulo, BrazilCausal Inference in Fintech? For Brave and True OnlyFrom rural Brazil to one of the country’s largest banks, Matheus’ journey could inspire many. Similarly to our previous guest, Iyar Lin, Matheus was interested in politics, but switched to economics, where he fell in love with math. Observing the state of the industry, he quickly realized that without causa...
Send us a Text Message.Support the showVideo version available on YouTube Recorded on Sep 13, 2023 in Beit El'Azari, Israel The eternal dance between the data and the modelEarly in his career, Iyar realized that purely associative models cannot provide him with the answers to the questions he found most interesting. This realization laid the groundwork for his search for methods that go beyond statistical summaries of the data. What started as a lonely journey, led him to become a data scienc...
![[Extra]: Mosquitos, Pascal & Hedge Funds || A Walk with Darko Matovski, PhD (causaLens) in London (2024)](https://storage.buzzsprout.com/w1dk33de06vqbo2gkjrtda4pcsnn?.jpg "[Extra]: Mosquitos, Pascal & Hedge Funds || A Walk with Darko Matovski, PhD (causaLens) in London (2024)")
Send us a Text Message.Support the showVideo version available on YouTubeRecorded on Sep 4, 2023 in London, UKA causal betDarko's story begins in Eastern Europe, where his early attempts in building a business and the influence of early-stage role models shaped his attitudes and helped him move through challenging and lonely moments in his career. See how mosquitos, Pascal programming language, and problems with generalization in vision models inspired Darko to build a company that helps some...
Send us a Text Message.Support the showVideo version of this episode is available hereRecorded on Sep 5, 2023 in Oxford, UKHave you ever wondered if we can answer seemingly unanswerable questions? Jakob's journey into causality started when he was 12 years old. Deeply dissatisfied with what adults had to offer when asked about the sources of causal knowledge, he started to look for the answers on his own. He studied philosophy, politics and economics to find his place at UCL's Centre for Arti...
Send us a Text Message.Support the showVideo version available on YouTube Recorded on Nov 29, 2023 in Cambridge, UKShould we continue to ask why? Alicia's machine learning journey began with... causal machine learning. Starting with econometrics, she discovered semi-parametric methods and the Pearlian framework at later stages of her career and incorporated both in her everyday toolkit. She loves to understand why things work, which inspires her to ask "why" not only in the context of treatme...
Send us a Text Message.Support the showVideo version available on YouTubeRecorded on Aug 29, 2023 in München, GermanyCan we meaningfully talk about causality in dynamical systems?Some people are puzzled when it comes to dynamical systems and the idea of causation.Dynamical systems well-known in physics, social sciences, and biology are often thought of as a special family of systems, where it might be difficult to meaningfully talk about causal direction. Naftali Weinberger devoted his career...
Send us a Text Message.Support the showVideo version available on YouTubeRecorded on Aug 27, 2023 in München, GermanyIs Causality Necessary For Autonomous Driving?From a child experimenter to a lead engineer working on a general causal inference engine, Daniel's choices have been marked by intense curiosity and the courage to take risks.Daniel shares how working with mathematicians differs from working with physicists and how having both on the team makes the team stronger. We discuss the jou...
Send us a Text Message.Support the show Video version available on YouTubeRecorded 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 ...
Send us a Text Message.Support the show`from causality import solution`Recorded on Sep 04, 2023 in London, United KingdomA 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...
Send us a Text Message.Support the showVideo version of this episode is available on YouTubeRecorded on Aug 24, 2023 in Berlin, GermanyDoes 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 modelin...
Send us a Text Message.Support the showVideo version of this episode available on YouTubeRecorded on Aug 14, 2023 in Frankfurt, GermanyAre 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 con...
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