Causal Bandits Podcast

Episodios

• 
  Spaceflight Epidemiologist's Grand DAG | Robert Reynolds, PhD S3E01 | CausalBanditsPodcast.com 28.09.2026 54m

  Send us Fan Mail Join the UBC Causal AI Cluster launch event In-person & online on October 8, 2026 More on the event here How do you build a causal model when only a few hundred people in history have ever been your population? Robert J. Reynolds was brought into NASA to answer questions difficult to settle with a simple experiment: what threatens astronauts health and how to make sure that countering one risk does not increase another one. To answer these questions, Rob worked with...
• 
  Can You Trust It? (One Day, 140K Downloads) | Isaac Gerber S2E12 | CausalBanditsPodcast.com 13.07.2026 56m

  Send us Fan Mail How do you trust causal inference code that no human has read? On New Year's Day this year, Isaac Gerber was a little bored. A week later he had shipped diff-diff, a difference-in-differences library that has since crossed 140,000 downloads, built almost entirely by AI agents. In this conversation we get into how he makes causal inference software he can actually stand behind, even when he never reads the code. In this episode, we cover: How Isaac built diff-diff, a differ...
• 
  Strait of Hormuz: Causal Models for Rare Events | Alexander Denev S2E11 | CausalBanditsPodcast.com 01.06.2026 43m

  Send us Fan Mail *How do you forecast an event that has never happened before?* How do you forecast an event that has never happened before? The recent closure and reopening of the Strait of Hormuz are unique events. For events like these, traditional risk models lose their statistical basis: repetition. Alexander Denev returns to the podcast to show how causal models (Bayesian networks) let us reason about rare events despite this limitation. In this episode, we cover: - Why value-at-ris...
• 
  Causality, Experimentation, and Marketplaces | Lawrence De Geest S2E10 01.04.2026 1h 5m

  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...
• 
  Do Heterogeneous Treatment Effects Exist? | Stephen Senn X Richard Hahn S2E9 | CausalBanditsPodcast 30.01.2026 1h 7m

  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 ...
• 
  Causal Inference & the "Bayesian-Frequentist War" | Richard Hahn S2E8 | CausalBanditsPodcast.com 27.12.2025 1h 24m

  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...
• 
  The Causal Gap: Truly Responsible AI Needs to Understand the Consequences | Zhijing Jin S2E7 30.10.2025 1h 3m

  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 ...
• 
  Create Your Causal Inference Roadmap. Causal Inference, TMLE & Sensitivity | Mark van der Laan S2E6 | CausalBanditsPodcast.com 22.09.2025 1h 29m

  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...
• 
  Causal Inference, Human Behavior, Science Crisis & The Power of Causal Graphs | Julia Rohrer S2E5 | CausalBanditsPodcast.com 04.06.2025 1h 21m

  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! ---...
• 
  MSFT Scientist: Agents, Causal AI & Future of DoWhy | Amit Sharma S2E4 | CausalBanditsPodcast.com 14.04.2025 1h 10m

  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...
• 
  Causal Secrets of N=1 Experiments | Eric Daza S2E3 | CausalBanditsPodcast.com 31.03.2025 1h 1m

  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...
• 
  From Quantum Physics to Causal AI at Spotify | Ciarán Gilligan-Lee S2E2 | CausalBanditsPodcast.com 29.01.2025 52m

  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...
• 
  49% Less Loss with Causal ML | Stefan Feuerriegel S2E1 | CausalBanditsPodcast.com 17.01.2025 28m

  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...
• 
  Causal AI at cAI 2024 London | CausalBanditsPodcast.com 09.12.2024 20m

  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...
• 
  Causal Bandits @ CLeaR 2024 | Part 2 | CausalBanditsPodcast.com 28.10.2024 22m

  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...
• 
  Causal Bandits @ CLeaR 2024 | Part 1 | CausalBanditsPodcast.com 07.10.2024 22m

  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...
• 
  Causal Bandits @ AAAI 2024 | Part 2 | CausalBanditsPodcast.com 23.09.2024 22m

  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:...
• 
  Causal Bandits @ AAAI 2024 | Part 1 | CausalBanditsPodcast.com 10.09.2024 19m

  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...
• 
  Free Will, LLMs & Intelligence | Judea Pearl Ep 21 | CausalBanditsPodcast.com 12.08.2024 54m

  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 ...
• 
  Causal AI & Individual Treatment Effects | Scott Mueller Ep. 20 | CausalBanditsPodcast.com 22.07.2024 52m

  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,...