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Recent episodes
Causality, Experimentation, and Marketplaces | Lawrence De Geest S2E10
Apr 1, 2026
1h 05m 00s
Do Heterogeneous Treatment Effects Exist? | Stephen Senn X Richard Hahn S2E9 | CausalBanditsPodcast
Jan 30, 2026
1h 07m 30s
Causal Inference & the "Bayesian-Frequentist War" | Richard Hahn S2E8 | CausalBanditsPodcast.com
Dec 27, 2025
1h 24m 18s
The Causal Gap: Truly Responsible AI Needs to Understand the Consequences | Zhijing Jin S2E7
Oct 30, 2025
1h 03m 17s
Create Your Causal Inference Roadmap. Causal Inference, TMLE & Sensitivity | Mark van der Laan S2E6 | CausalBanditsPodcast.com
Sep 22, 2025
1h 29m 40s
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| Date | Episode | Description | Length | ||||||
|---|---|---|---|---|---|---|---|---|---|
| 4/1/26 |  Causality, Experimentation, and Marketplaces | Lawrence De Geest S2E10 | Send us Fan Mail Causality, Experimentation, and Marketplaces Meet Lawrence de Geest (Zoox, ex-Lyft, ex-NBA), a former soccer player and an ex-NBA data scientist, who fell in love with marketplaces, despite the fact he hated math. In the episode we ponder how to deal with causality when our interventions change the dynamics of the environment we intervene upon, what to do with SUTVA violations, and how to design efficient quasi-experiments. - Why simple A/B tests fail at marketplaces - How... | 1h 05m 00s | ||||||
| 1/30/26 |  Do Heterogeneous Treatment Effects Exist? | Stephen Senn X Richard Hahn S2E9 | CausalBanditsPodcast | Send us Fan Mail Do Heterogeneous Treatment Effects Exist? For the last 50 years, we've designed cars to be safe... For the 50th-percentile male. Well, that's actually not 100% correct. According to Stanford's report, we introduced "female" crash test dummies in the 1960s, but... They were just scaled-down versions of male dummies and... Represented the 5th percentile of females in terms of body size and mass (aka the smallest 5% of women in the general population). These dummies also ... | 1h 07m 30s | ||||||
| 12/27/25 |  Causal Inference & the "Bayesian-Frequentist War" | Richard Hahn S2E8 | CausalBanditsPodcast.com | Send us Fan Mail *What can we learn about causal inference from the “war” between Bayesians and frequentists?* What can we learn about causal inference from the “war” between Bayesians and frequentists? In the episode, we cover: - What can we learn from the “war” between Bayesians and frequentists? - Why do Bayesian Additive Regression Trees (BART) “just work”? - Do heterogeneous treatment effects exist? - Is RCT generalization a heterogeneity problem? In the episode, we accidentally coin... | 1h 24m 18s | ||||||
| 10/30/25 |  The Causal Gap: Truly Responsible AI Needs to Understand the Consequences | Zhijing Jin S2E7 | Send us Fan Mail The Causal Gap: Truly Responsible AI Needs to Understand the Consequences Why do LLMs systematically drive themselves to extinction, and what does it have to do with evolution, moral reasoning, and causality? In this brand-new episode of Causal Bandits, we meet Zhijing Jin (Max Planck Institute for Intelligent Systems, University of Toronto) to answer these questions and look into the future of automated causal reasoning. In this episode, we discuss: - Zhijing's new work ... | 1h 03m 17s | ||||||
| 9/22/25 |  Create Your Causal Inference Roadmap. Causal Inference, TMLE & Sensitivity | Mark van der Laan S2E6 | CausalBanditsPodcast.com | Send us Fan Mail Create Your Causal Inference Roadmap. Causal Inference, TMLE & Sensitivity If you're into causal inference and machine learning you probably heard about double machine learning (DML). DML is one of the most popular frameworks leveraging machine learning algorithms for causal inference, while offering good statistical properties. Yet... There's another framework that also leverages machine learning for causal inference that was created years earlier. Welcome to the wo... | 1h 29m 40s | ||||||
| 6/4/25 |  Causal Inference, Human Behavior, Science Crisis & The Power of Causal Graphs | Julia Rohrer S2E5 | CausalBanditsPodcast.com | Send us Fan Mail *Causal Inference From Human Behavior, Reproducibility Crisis & The Power of Causal Graphs* Is Jonathan Heidt right that social media causes the mental health crisis in young people? If so, how can we be sure? Can other disciplines learn something from the reproducibility crisis in Psychology, and what is multiverse analysis? Join us for a conversation on causal inference from human behavior, the reproducibility crisis in sciences, and the power of causal graphs! ---... | 1h 21m 54s | ||||||
| 4/14/25 |  MSFT Scientist: Agents, Causal AI & Future of DoWhy | Amit Sharma S2E4 | CausalBanditsPodcast.com | Send us Fan Mail *Agents, Causal AI & The Future of DoWhy* The idea of agentic systems taking over more complex human tasks is compelling. New "production-grade" frameworks to build agentic systems pop up, suggesting that we're close to achieving full automation of these challenging multi-step tasks. But is the underlying agentic technology itself ready for production? And if not, can LLM-based systems help us making better decisions? Recent new developments in the DoWhy/PyWhy ecosys... | 1h 10m 10s | ||||||
| 3/31/25 |  Causal Secrets of N=1 Experiments | Eric Daza S2E3 | CausalBanditsPodcast.com | Send us Fan Mail 📽️ FREE Online Course on Causality 📕 Causal Inference & Discovery in Python Causal Secrets of N=1 Experiments Join me for a one of a kind conversation on the opportunities and challenges of n-of-1 trials, Eric's causal journey, his path into statistics, his love of sci-fi, and how single-subject experiments could reshape personalized medicine. Video version available here About The Guest Dr. Eric J. Daza is a biostatistician and health data scientist with... | 1h 01m 14s | ||||||
| 1/29/25 |  From Quantum Physics to Causal AI at Spotify | Ciarán Gilligan-Lee S2E2 | CausalBanditsPodcast.com | Send us Fan Mail From Quantum Causal Models to Causal AI at Spotify Ciarán loved Lego. Fascinated by the endless possibilities offered by the blocks, he once asked his parents what he could do as an adult to keep building with them. The answer: engineering. As he delved deeper into engineering, Ciarán noticed that its rules relied on a deeper structure. This realization inspired him to pursue quantum physics, which eventually brought him face-to-face with fundamental questions about causa... | 52m 10s | ||||||
| 1/17/25 |  49% Less Loss with Causal ML | Stefan Feuerriegel S2E1 | CausalBanditsPodcast.com | Send us Fan Mail Stefan Feuerriegel is the Head of the Institute of AI in Management at LMU. His team consistently publishes work on causal machine learning at top AI conferences, including NeurIPS, ICML, and more. At the same time, they help businesses implement causal methods in practice. They worked on projects with companies like ABB Hitachi, and Booking.com. Stefan believes his team thrives because of its diversity and aims to bring more causal machine learning to medicine. I had a great... | 28m 35s | ||||||
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| 12/9/24 |  Causal AI at cAI 2024 London | CausalBanditsPodcast.com | Send us Fan Mail Causal Bandits at cAI 2024 (The Royal Society, London) The cAI Conference in London slammed the door on baseless claims that causality cannot be used in industrial practice. In the episode of Causal Bandits Extra we interview participants and speakers at Causal AI Conference London, who share their main insights from the event, and the challenges they face in applying causal methods in their everyday work. Time codes: 00:29 - Eyal Kazin (Zimmer Biomet) 01:44 - Athanasios V... | 20m 01s | ||||||
| 10/28/24 |  Causal Bandits @ CLeaR 2024 | Part 2 | CausalBanditsPodcast.com | Send us Fan Mail Which models work best for causal discovery and double machine learning? In this extra episode, we present 4 more conversations with the researchers presenting their work at the CLeaR 2024 conference in Los Angeles, California. What you'll learn: - Which causal discovery models perform best with their default hyperparameters? - How to tune your double machine learning model? - Does putting your paper on ArXiv early increase its chances of being accepted at a conference? - H... | 22m 05s | ||||||
| 10/7/24 |  Causal Bandits @ CLeaR 2024 | Part 1 | CausalBanditsPodcast.com | Send us Fan Mail Root cause analysis, model explanations, causal discovery. Are we facing a missing benchmark problem? Or not anymore? In this special episode, we travel to Los Angeles to talk with researchers at the forefront of causal research, exploring their projects, key insights, and the challenges they face in their work. Time codes: 0:15 - 02:40 Kevin Debeire 2:41 - 06:37 Yuchen Zhu 06:37 - 10:09 Konstantin Göbler 10:09 - 17:05 Urja Pawar 17... | 22m 11s | ||||||
| 9/23/24 |  Causal Bandits @ AAAI 2024 | Part 2 | CausalBanditsPodcast.com | Send us Fan Mail *Causal Bandits at AAAI 2024 || Part 2* In this special episode we interview researchers who presented their work at AAAI 2024 in Vancouver, Canada. Time codes: 00:12 - 04:18 Kevin Xia (Columbia University) - Transportability 4:19 - 9:53 Patrick Altmeyer (Delft) - Explainability & black-box models 9:54 - 12:24 Lokesh Nagalapatti (IIT Bombay) - Continuous treatment effects 12:24 - 16:06 Golnoosh Farnadi (McGill University) - Causality & responsible AI 16:... | 22m 38s | ||||||
| 9/10/24 |  Causal Bandits @ AAAI 2024 | Part 1 | CausalBanditsPodcast.com | Send us Fan Mail Causal Bandits at AAAI 2024 || Part 1 In this special episode we interview researchers who presented their work at AAAI 2024 in Vancouver, Canada and participants of our workshop on causality and large language models (LLMs) Time codes: 00:00 Intro 00:20 Osman Ali Mian (CISPA) - Adaptive causal discovery for time series 04:35 Emily McMilin (Independent/Meta) - LLMs, causality & selection bias 07:36 Scott Mueller (UCLA) - Causality for EV incentives 12:41 Andrew La... | 19m 35s | ||||||
| 8/12/24 |  Free Will, LLMs & Intelligence | Judea Pearl Ep 21 | CausalBanditsPodcast.com | Send us Fan Mail Meet The Godfather of Modern Causal Inference His work has pretty literally changed the course of my life and I am honored and incredibly grateful we could meet for this great conversation in his home in Los Angeles To anybody who knows something about modern causal inference, he needs no introduction. He loves history, philosophy and music, and I believe it's fair to say that he's the godfather of modern causality. Ladies & gentlemen, please welcome, professor Judea ... | 54m 07s | ||||||
| 7/22/24 |  Causal AI & Individual Treatment Effects | Scott Mueller Ep. 20 | CausalBanditsPodcast.com | Send us Fan Mail Can we say something about YOUR personal treatment effect? The estimation of individual treatment effects is the Holy Grail of personalized medicine. It's also extremely difficult. Yet, Scott is not discouraged from studying this topic. In fact, he quit a pretty successful business to study it. In a series of papers, Scott describes how combining experimental and observational data can help us understand individual causal effects. Although this sounds enigmatic to many,... | 52m 40s | ||||||
| 7/1/24 |  Causal AI in Personalization | Dima Goldenberg Ep 19 | CausalBanditsPodcast.com | Send us Fan Mail Video version of this episode is available here Causal personalization? Dima did not love computers enough to forget about his passion for understanding people. His work at Booking.com focuses on recommender systems and personalization, and their intersection with AB testing, constrained optimization and causal inference. Dima's passion for building things started early in his childhood and continues up to this day, but recent events in his life also bring new opportuniti... | 1h 06m 40s | ||||||
| 6/17/24 |  Causal Inference for Drug Repurposing & CausalLib | Ehud Karavani Ep 18 | CausalBanditsPodcast.com | Send us Fan Mail Was Deep Learning Revolution Bad For Causal Inference? Did deep learning revolution slowed down the progress in causal research? Can causality help in finding drug repurposing candidates? What are the main challenges in using causal inference at scale? Ehud Karavani, the author of the CausalLib Python library and Researcher at IBM Research shares his experiences and thoughts on these challenging questions. Ehud believes in the power of good code, but for him code is not ... | 53m 32s | ||||||
| 6/3/24 |  From Physics to Causal AI & Back | Bernhard Schölkopf Ep 17 | CausalBanditsPodcast.com | Send us Fan Mail 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 ... | 34m 20s | ||||||
| 5/20/24 |  Open Source Causal AI & The Generative Revolution | Emre Kıcıman Ep 16 | CausalBanditsPodcast.com | Send us Fan Mail 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... | 42m 20s | ||||||
| 5/6/24 |  Why Hinton Was Wrong, Causal AI & Science | Thanos Vlontzos Ep 15 | CausalBanditsPodcast.com | Send us Fan Mail 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 i... | 1h 05m 29s | ||||||
| 4/15/24 |  Causal Inference & Financial Modeling with Alexander Denev Ep 14 | CausalBanditsPodcast.com | Send us Fan Mail 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 Guest Alexander Denev is the CEO of Turnleaf Analytics. He's an author of multiple books on financial modeling and a former Head of AI (Financial... | 1h 04m 09s | ||||||
| 4/1/24 |  Causal Inference & Reinforcement Learning with Andrew Lampinen Ep 13 | CausalBanditsPodcast.com | Send us Fan Mail Love Causal Bandits Podcast? Help us bring more quality content: Support the show 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.... | 55m 24s | ||||||
| 3/18/24 |  Causal Inference, Clinical Trials & Randomization || Stephen Senn || Causal Bandits Ep. 012 (2024) | Send us Fan Mail Support the show Video version available on YouTube Do 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 ... | 1h 18m 05s | ||||||
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