Causal Bandits Podcast

Send us Fan MailCausality, Experimentation, and MarketplacesMeet 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 reversing synthetic controls logic can help us design better experiments- Why Lawrence thinks that average treatment effect is just a snapshot of here and now- How Magellan used data science to prove that Portugal was harvesting spices on Spanish territory------------------------------------------------------------------------------------------------------Video version available on YouTube: https://youtu.be/acCy16L33tURecorded in 2026 in San Francisco, USA.------------------------------------------------------------------------------------------------------About The GuestLawrence De Geest is an economist and data scientist at Zoox. He was previously a data scientist at Lyft and the NBA, and before joining industry, an Assistant Professor at Suffolk University, with visiting appointments at Boston College and the University of San Francisco. His main research interests are marketplaces, collective action and experimentation. Outside of work he loves biking, surfing, and playing with his dog.Connect with Lawrence:- Lawrence on LinkedIn: https://www.linkedin.com/in/lawrence-de-geest-21a206a/- Lawrence's web page: https://lrdegeest.github.io/About The HostAleksander (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: https://bit.ly/aleksander-molakSupport the showCausal Bandits PodcastCausal AI || Causal Machine Learning || Causal Inference & DiscoveryWeb: https://causalbanditspodcast.comConnect on LinkedIn:   https://www.linkedin.com/in/aleksandermolak/Join Causal Python Weekly: https://causalpython.io  The Causal Book: https://amzn.to/3QhsRz4

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.

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!

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.

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.

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.

Amit Sharma, Principal Researcher at Microsoft Research and co-creator of the DoWhy library, discusses the future of agentic systems and their impact on complex human tasks. He highlights the challenges in current frameworks, particularly around verification, while emphasizing innovative approaches in causal modeling. The conversation touches on integrating large language models to improve decision-making and the creation of the Duy library for causal inference, aiming to enhance accessibility for newcomers in the field. This engaging dialogue showcases the intersection of causality and AI in shaping robust systems.

In this engaging discussion, biostatistician Eric Daza shares insights from his 22 years of experience in health data science. He delves into the fascinating world of n-of-1 trials, emphasizing how personalized experiments can transform health insights. Eric highlights the challenges of causal inference and the impact of historical treatments on outcomes. He also shares his journey from neurobiology to statistics, his love for sci-fi, and practical tips for conducting effective self-experiments, inspiring a new era in personalized medicine.

Ciarán Gilligan-Lee, Head of the Causal Inference Research Lab at Spotify and an Honorary Associate Professor at UCL, delves into fascinating intersections of quantum physics and causal AI. He discusses how understanding causality can enhance business outcomes at Spotify while unraveling the complexities of causal inference versus correlation. Ciarán also shares insights from his innovative work combining causal methods with astrophysics, exploring galaxy evolution and environmental factors in star formation, all while reflecting on influential literature that shaped his journey.

Stefan Feuerriegel, Head of the Institute of AI in Management at LMU, discusses the exciting world of causal machine learning. He shares insights from successful projects that improved semiconductor yields and enhanced healthcare outcomes through causal methods. Stefan emphasizes the importance of team diversity in problem-solving and the necessity of tailoring complex ideas for broader audiences. He also offers practical advice for decision-makers and highlights the transformative potential of collaboration in leveraging AI for better decision-making.