Causal Bandits Podcast
Alex Molak
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 a machine learning engineer, best-selling author, and an educator who decided to travel the world to record conversations with the most interesting minds in causality to share them with you.Enjoy and stay causal!Keywords: Causal AI, Causal Machine Learning, Causality, Causal Inference, Causal Discovery, Machine Learning, AI, Artificial Intelligence
Episodes
Mentioned books
7 snips
Oct 12, 2026 • 1h 23min
AI's Fluency Is Not Understanding | Elias Bareinboim S3E02 | CausalBanditsPodcast.com
Elias Bareinboim, a Columbia computer science professor and leader in causal AI, explores why fluent AI can still lack genuine understanding. They discuss the unplugged-lamp problem, the limits of correlation, verifiable rewards in math and coding, causal reinforcement learning, counterfactual bandits, multi-agent strategy, safety, and why future world models must represent interventions and mechanisms.
14 snips
Sep 28, 2026 • 55min
Spaceflight Epidemiologist's Grand DAG | Robert Reynolds, PhD S3E01 | CausalBanditsPodcast.com
Robert Reynolds, a spaceflight epidemiologist, data scientist, and former NASA health researcher, explores how to model astronaut risks when only a tiny, highly selected population exists. They discuss building living DAGs across disciplines, turning expert knowledge into testable graphs, resolving scientific disagreements, and tackling positivity and uncertainty in high-stakes spaceflight research.
14 snips
Jul 13, 2026 • 56min
Can You Trust It? (One Day, 140K Downloads) | Isaac Gerber S2E12 | CausalBanditsPodcast.com
Isaac Gerber, a data science leader who builds open-source causal inference tools, talks about creating the DiffDiff difference-in-differences library in one day using AI agents. He outlines a multi-step agent workflow, cross-language validation with R, and strategies to detect silent failures like dropped covariates. He also explains why verification, not coding, is now the main bottleneck.
Jun 1, 2026 • 43min
Strait of Hormuz: Causal Models for Rare Events | Alexander Denev S2E11 | CausalBanditsPodcast.com
Alexander Denev, CEO of Turnleaf Analytics and author on Bayesian networks, explores using causal models to reason about one-off shocks. He explains why correlation-based risk breaks down and how Bayesian networks can hold through time. The conversation covers using LLMs as scalable expert surrogates, historical analogies like the Bosphorus, and a three-way robustness test for scenario models.
16 snips
Apr 1, 2026 • 1h 5min
Causality, Experimentation, and Marketplaces | Lawrence De Geest S2E10
Lawrence De Geest, economist and data scientist at Zoox (formerly Lyft and the NBA), riffs on marketplaces and experimentation. He explains why simple A/B tests fail in marketplaces. He walks through coarse randomization, synthetic-control quasi-experiments, and designs to handle interference. He also discusses supply elasticity, long-term re-equilibration, and practical trade-offs in real-world experiments.
15 snips
Jan 30, 2026 • 1h 8min
Do Heterogeneous Treatment Effects Exist? | Stephen Senn X Richard Hahn S2E9 | CausalBanditsPodcast
Stephen Senn, medical statistician focused on drug development and trials, and Richard Hahn, ASU statistics professor working on causal inference and regression trees, debate whether heterogeneous treatment effects are real and detectable. They discuss ethics of averaging, richer covariate measurement for discovery, machine learning on RCT data, trial design tradeoffs, and when subgroup findings become actionable.
79 snips
Dec 27, 2025 • 1h 24min
Causal Inference & the "Bayesian-Frequentist War" | Richard Hahn S2E8 | CausalBanditsPodcast.com
In this enlightening discussion, Professor Richard Hahn from Arizona State University delves into the ongoing debate between Bayesians and frequentists in statistics. He shares insights on why Bayesian Additive Regression Trees (BART) are effective and how they compare to models like XGBoost. The conversation uncovers the significance of heterogeneous treatment effects and the challenges in generalizing RCT results. Richard emphasizes the importance of realistic simulation studies for understanding causal inference, while coining the term "feature-level selection bias"—a must-listen for stats enthusiasts!
19 snips
Oct 30, 2025 • 1h 3min
The Causal Gap: Truly Responsible AI Needs to Understand the Consequences | Zhijing Jin S2E7
In this discussion, Zhijing Jin, a leading research scientist at the Max Planck Institute and incoming Assistant Professor at the University of Toronto, dives into the critical intersection of causality and AI ethics. She explores why LLMs often falter in their decision-making and the importance of causal reasoning in moral frameworks. Highlighting her work on multi-agent simulations, she reveals troubling patterns of self-destructive behavior in AI models. Zhijing also emphasizes the need for interdisciplinary research and greater awareness of causal understanding in AI to foster responsible development.
11 snips
Sep 22, 2025 • 1h 30min
Create Your Causal Inference Roadmap. Causal Inference, TMLE & Sensitivity | Mark van der Laan S2E6 | CausalBanditsPodcast.com
Mark van der Laan, a renowned professor at UC Berkeley and the mastermind behind Targeted Maximum Likelihood Estimation (TMLE), dives deep into causal inference. He differentiates between TMLE and double machine learning, emphasizing their unique applications. Mark shares insights on building a stepwise causal roadmap and the importance of uncertainty quantification. He discusses practical applications of his work and reflects on the role of large language models in research. His advice encourages diversity and rigor in the causal inference community.
6 snips
Jun 4, 2025 • 1h 22min
Causal Inference, Human Behavior, Science Crisis & The Power of Causal Graphs | Julia Rohrer S2E5 | CausalBanditsPodcast.com
Julia Rohrer, a personality psychologist at the University of Leipzig and senior editor of Psychological Science, dives into fascinating topics like the reproducibility crisis in psychology and how it may relate to a broader scientific discourse. She critiques the impact of social media on youth mental health and discusses the intricacies of establishing causal inferences. The conversation also covers the significance of multiverse analysis, using birth order and personality traits as a case study, and the importance of Directed Acyclic Graphs in psychological education.
