Causal Bandits Podcast

Hosted by Alex Molak

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Sep 2026

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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

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September 28, 2026Episode 154 min

Spaceflight Epidemiologist's Grand DAG | Robert Reynolds, PhD S3E01 | CausalBanditsPodcast.com

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 physicians, engineers, physiologists, and statisticians, none of whom speak quite the same language. What came out of it is one of the most ambitious causal modeling efforts in spaceflight history. In this episode, we cover: - How to successfully build a causal model in a multidisciplinary organization - Why ignorance is important in the scientific process - What role testable implications play in the development of a causal graph - What to do when your sample size is tiny and the stakes are high Enjoy the episode! Recorded on Sep 21, 2026 in New York City, US. About The Guest Dr. Robert J. Reynolds is a spaceflight epidemiologist and data scientist working at the intersection of human spaceflight, small-n methodology, and causal inference. At NASA's Johnson Space Center he supported the Human Health and Performance Directorate, applying statistical modeling, systems thinking, machine learning, and network science to decisions about astronaut health and mission readiness. He is also Associate Professor of Aerospace Medicine at the University of Central Florida College of Medicine. He holds a PhD and an MPH in epidemiology and an MS in statistics, and is an Accredited Professional Statistician (PStat) with the American Statistical Association. Rob delivers the keynote at the launch of UBC's causal AI research cluster, "Graphical Models in AI: Discovery and Application," on October 8, 2026. Connect with Robert: - Robert on LinkedIn: linkedin.com/in/RobertReynoldsPhD About The Host Aleksander (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-molak Links - Reynolds, R. J. (2026) - "Living DAGs: the future of DAGs in epidemiology " (https://pubmed.ncbi.nlm.nih.gov/41662841/) - Antonsen, Reynolds, et al. (2024) - "Causal diagramming for assessing human system risk in spaceflight" (https://www.nature.com/articles/s41526-024-00375-7) - Ward, Reynolds, et al. (2024) - "Levels of evidence for human system risk evaluation" (https://www.nature.com/articles/s41526-024-00372-w) 💠 Alex's Book on Causality 🔷 Consulting and Causal AI Training For Your Team: hello@causalpython.io YouTube Playlist Support the show Causal Bandits Podcast Causal AI || Causal Machine Learning || Causal Inference & Discovery Web: https://causalbanditspodcast.com Connect on LinkedIn: https://www.linkedin.com/in/aleksandermolak/ Join Causal Python Weekly: https://causalpython.io The Causal Book: https://amzn.to/3QhsRz4

July 13, 2026Episode 1256 min

Can You Trust It? (One Day, 140K Downloads) | Isaac Gerber S2E12 | CausalBanditsPodcast.com

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 difference-in-differences library, in a single day (now 140,000+ downloads) A five-step workflow for building causal inference software you can actually trust Why he builds with one model family and validates with another How silent failures, like quietly dropped covariates, slip into AI-written code, and how to catch them Why verification, not writing code, is becoming the real bottleneck Enjoy the episode! ------------------------------------------------------------------------------------------------------ Video version available on YouTube: https://youtu.be/O53Ra0iIFp8 Recorded on Apr 28, 2026 in New York, USA. ------------------------------------------------------------------------------------------------------ About The Guest Isaac Gerber is a data science leader focused on causal inference methodology and the open-source tooling around it. Isaac has 20 years of experience at the intersection of data, analytics, and business. Connect with Isaac: Isaac on LinkedIn: / isaac-gerber Isaac on GitHub: https://github.com/igerber Isaac's web page: https://igerber.com/ About The Host Aleksander (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-molak Links Web https://github.com/igerber Papers Gerber, I. (2026) - "Design-Based Variance Estimation for Modern Heterogeneity-Robust Difference-in-Differences Estimators" ( https://arxiv.org/abs/2605.04124 ) Let's connect! 👉🏼 Linkedin: / aleksandermolak 👉🏼 Bluesky: https://alxndrmlk.bsky.social 👉🏼 Tiktok: / alex.molak Business 👉🏼 Consulting and Causal AI Training For Your Team: hello@causalpython.io #machinelearning #causalai #causalinference #causality Support the show Causal Bandits Podcast Causal AI || Causal Machine Learning || Causal Inference & Discovery Web: https://causalbanditspodcast.com Connect on LinkedIn: https://www.linkedin.com/in/aleksandermolak/ Join Causal Python Weekly: https://causalpython.io The Causal Book: https://amzn.to/3QhsRz4

June 1, 2026Episode 1143 min

Strait of Hormuz: Causal Models for Rare Events | Alexander Denev S2E11 | CausalBanditsPodcast.com

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-risk and other correlation-based models break exactly when you need them most - How a causal structure can "hold in time" - Building scenarios with LLMs - benefits, drawbacks, and lessons learned - Historical analogy as a modeling tool: Bosphorus, Hormuz, and more - A three-way robustness test for any Bayesian network - How the model's call held up: a ceasefire, a still-closed strait, and lasting infrastructure damage keeping oil elevated "History doesn't repeat itself, but it rhymes." ------------------------------------------------------------------------------------------------------ Video version available on the Youtube: https://youtu.be/FzKy2ws-7qs Recorded on May 29, 2026 in London, UK. ------------------------------------------------------------------------------------------------------ *About The Guest* Alexander Denev works at the intersection of quantitative finance, causality, and AI. He's the CEO of Turnleaf Analytics and the author of two books on applying Bayesian networks and probabilistic graphical models to finance and scenario analysis. Connect with Alexander: - Alexander on LinkedIn: https://www.linkedin.com/in/alexander-denev-66a25824/ - Alexander's web page: https://turnleafanalytics.com/ *About The Host* Aleksander (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-molak *Links* Web - Alexander's LinkedIn post, Bayesian-network scenario for the Strait of Hormuz / Israel-Iran-US conflict: https://www.linkedin.com/posts/alexander-denev-66a25824_when-modelling-the-impact-of-events-that-share-7442892381668048896-JDs5/ - Risk.net article, "Iran confusion makes the case for causal modelling": https://www.risk.net/our-take/7963361/iran-confusion-makes-the-case-for-causal-modelling Books - Rebonato, R. & Denev, A. - Portfolio Management under Stress: A Bayesian-Net Approach to Coherent Asset Allocation ( https://amzn.to/3vE6Jc1 ) - López de Prado, M. - Advances in Financial Machine Learning ( https://amzn.to/3PXD8kH) - Molak, A. - Causal Inference and Discovery in Python ( https://amzn.to/3VVK4m3 ) - Denev, A. - Probabilistic Graphical Models: A New Way of Thinking in Financial Modelling ( https://amzn.to/3VQeLJm ) - Pearl, J. & Mackenzie, D. - The Book of Why (recommended entry point) ( https://amzn.to/4e0ATrZ ) - Pearl, J. - Causality: Models, Reasoning and Inference (for advanced readers) ( https://amzn.to/49zBKf5 ) - Rebonato, R. - Coherent Stress Testing: A Bayesian Approach to the Analysis of Financial Stress ( https://amzn.to/3RC411e ) *Perks & resources* 🚀 Join FREE Causal Python Weekly Newsletter: https://causalpython.io 📽️ FREE Online Course on Causality: https://causalsecrets.com/ 📕 My Book on Causality: https://amzn.to/3SKRXIw 🔥 Causal Bandits Community Beta Wait List: https://causalbandits.com/ 🎙️ Get notifications about new episodes: https://causalbanditspodcast.com *Let's connect!* 👉🏼 Linkedin: https://www.linkedin.com/in/aleksandermolak/ 👉🏼 Bluesky: https://alxndrmlk.bsky.social 👉🏼 Tiktok: https://www.tiktok.com/@alex.molak *Business* 👉🏼 Consulting and Causal AI Training For Your Team: hello@causalpython.io *Podcast Playlist* https://www.youtube.com/playlist?list=PLhKKv6iMja4p5FbJIgzTOE67E1M6c8lnB *Causal Bandits Team* Project Coordinator: Taiba Malik ( https://www.instagram.com/taibasplay/ ) Video and Audio Editing: Navneet Sharma #machinelearning #causalai #causalinference #causality Support the show Causal Bandits Podcast Causal AI || Causal Machine Learning || Causal Inference & Discovery Web: https://causalbanditspodcast.com Connect on LinkedIn: https://www.linkedin.com/in/aleksandermolak/ Join Causal Python Weekly: https://causalpython.io The Causal Book: https://amzn.to/3QhsRz4

April 1, 2026Episode 101 hr 5 min

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 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/acCy16L33tU Recorded in 2026 in San Francisco, USA. ------------------------------------------------------------------------------------------------------ About The Guest Lawrence 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 Host Aleksander (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-molak Support the show Causal Bandits Podcast Causal AI || Causal Machine Learning || Causal Inference & Discovery Web: https://causalbanditspodcast.com Connect on LinkedIn: https://www.linkedin.com/in/aleksandermolak/ Join Causal Python Weekly: https://causalpython.io The Causal Book: https://amzn.to/3QhsRz4

January 30, 2026Episode 91 hr 7 min

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 did not take into account female-typical injury tolerance, biomechanics, spinal alignment, and more. But... Does it matter for actual safety? In the episode, we cover: - Do heterogeneous treatment effects (different effects in different contexts) exist? - If so, can we actually detect them? - Is it more ethical to look for heterogeneous treatment effects or rather look at global averages? Video version available on the Youtube: https://youtu.be/V801RQTBpp4 Recorded on Nov 12, 2025 in Malaga, Spain. ------------------------------------------------------------------------------------------------------ About Richard Professor Richard Hahn, PhD, is a professor of statistics at Arizona State University (ASU). He develops novel statistical methods for analyzing data arising from the social sciences, including psychology, economics, education, and business. His current focus revolves around causal inference using regression tree models, as well as foundational issues in Bayesian statistics. Connect with Richard: - Richard on LinkedIn: https://www.linkedin.com/in/richard-hahn-a1096050/ About Stephen Stephen Senn, PhD, is a statistician and consultant who specializes in drug development clinical trials. He is a former Group Head at Ciba-Geigy and has taught at the University of Glasgow and University College London (UCL). He is the author of "Statistical Issues in Drug Development," "Crossover Trials in Clinical Research," and "Dicing with Death." Connect with Stephen: - Stephen on LinkedIn: https://www.linkedin.com/in/stephen-senn-67791322/ Support the show Causal Bandits Podcast Causal AI || Causal Machine Learning || Causal Inference & Discovery Web: https://causalbanditspodcast.com Connect on LinkedIn: https://www.linkedin.com/in/aleksandermolak/ Join Causal Python Weekly: https://causalpython.io The Causal Book: https://amzn.to/3QhsRz4

December 27, 2025Episode 81 hr 24 min

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 coined a new term: “feature-level selection bias.” ------------------------------------------------------------------------------------------------------ Video version available on the Youtube: https://youtu.be/-hRS8eU3Tow Recorded in Arizona, US. ------------------------------------------------------------------------------------------------------ *About The Guest* Professor Richard Hahn, PhD, is a professor of statistics at Arizona State University (ASU). He develops novel statistical methods for analyzing data arising from the social sciences, including psychology, economics, education, and business. His current focus revolves around causal inference using regression tree models, as well as foundational issues in Bayesian statistics. Connect with Richard: - Richard on LinkedIn: https://www.linkedin.com/in/richard-hahn-a1096050/ - Richard's web page: https://methodologymatters.substack.com/about *About The Host* Aleksander (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-molak *Links* Repo - https://stochtree.ai Papers - Hahn et al (2020) - "Bayesian Regression Tree Models for Causal Inference" (https://projecteuclid.org/journals/bayesian-analysis/volume-15/issue-3/Bayesian-Regression-Tree-Models-for-Causal-Inference--Regularization-Confounding/10.1214/19-BA1195.full) - Yeager, ..., Dweck et al (2019) - "A national experiment reveals where a growth mindset improves achievement" (https://www.nature.com/articles/s41586-019-1466-y) - Herren, Hahn, et al (2025) - "StochTree: BART-based modeling in R and Python" (https://arxiv.org/abs/2512.12051) Support the show Causal Bandits Podcast Causal AI || Causal Machine Learning || Causal Inference & Discovery Web: https://causalbanditspodcast.com Connect on LinkedIn: https://www.linkedin.com/in/aleksandermolak/ Join Causal Python Weekly: https://causalpython.io The Causal Book: https://amzn.to/3QhsRz4

October 30, 2025Episode 71 hr 3 min

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 on the "causal scientist" - What's missing in responsible AI - Why ethics matter for agentic systems - Is causality a necessary element of moral reasoning? ------------------------------------------------------------------------------------------------------ Video version available on Youtube: https://youtu.be/Frb6eTW2ywk Recorded on Aug 18, 2025 in Tübingen, Germany. ------------------------------------------------------------------------------------------------------ About The Guest Zhiijing Jin is a researcher scientist at Max Planck Institute for Intelligent Systems and an incoming Assistant Professor at the University of Toronto. Her work is focused on causality, natural language, and ethics, in particular in the context of large language models and multi-agent systems. Her work received multiple awards, including NeurIPS best paper award, and has been featured in CHIP Magazine, WIRED, and MIT News. She grew up in Shanghai. Currently she prepares to open her new research lab at the University of Toronto. Support the show Causal Bandits Podcast Causal AI || Causal Machine Learning || Causal Inference & Discovery Web: https://causalbanditspodcast.com Connect on LinkedIn: https://www.linkedin.com/in/aleksandermolak/ Join Causal Python Weekly: https://causalpython.io The Causal Book: https://amzn.to/3QhsRz4

September 22, 2025Episode 61 hr 29 min

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 world of targeted maximum likelihood estimation (TMLE). Our today's guest, Prof. Mark van der Laan (UC Berkeley) is the godfather of TMLE. In the episode, we discuss: - Similarities and differences between DML and TMLE - How to build a causal roadmap for your project - How Mark uses math to solve real-world problems - Why uncertainty quantification is so important ------------------------------------------------------------------------------------------------------ Video version available on the Youtube: https://youtu.be/qr5JolEAuJU Recorded on Sep 16, 2025 in Berkeley, California, US. ------------------------------------------------------------------------------------------------------ *About The Guest* Mark van der Laan is a Professor in Biostatistics and Statistics at UC Berkeley. He's the godfather of Targeted Maximum Likelihood Estimation (TMLE), a semiparametric framework that uses machine learning to estimate causal effects or other statistical parameters from observational data, and its new incarnation Targeted Machine Learning. *About The Host* Aleksander (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-molak *Links* Libraries - Deep LTMLE (Python): https://github.com/shirakawatoru/dltmle Papers - Dang, ..., van der Laan et al. (2023) - "A Causal Roadmap for Generating High-Quality Real-World Evidence" ( https://arxiv.org/abs/2305.06850 ) - Gruber, ..., van der Laan (2021) - "Developing a Targeted Learning-Based Statistical Analysis Plan" ( https://www.tandfonline.com/doi/full/10.1080/19466315.2022.2116104 ) Support the show Causal Bandits Podcast Causal AI || Causal Machine Learning || Causal Inference & Discovery Web: https://causalbanditspodcast.com Connect on LinkedIn: https://www.linkedin.com/in/aleksandermolak/ Join Causal Python Weekly: https://causalpython.io The Causal Book: https://amzn.to/3QhsRz4

June 4, 2025Episode 51 hr 21 min

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! ------------------------------------------------------------------------------------------------------ Audio version available on YouTube: https://youtu.be/YQetmI-y5gM Recorded on May 16, 2025, in Leipzig, Germany. ------------------------------------------------------------------------------------------------------ *About The Guest* Julia Rohrer, PhD, is a researcher and personality psychologist at the University of Leipzig. She's interested in the effects of birth order, age patterns in personality, human well-being, and causal inference. Her works have been published in top journals, including Nature Human Behavior. She has been an active advocate for increased research transparency, and she continues this mission as a senior editor of Psychological Science. Julia frequently gives talks about good practices in science and causal inference. You can read Julia's blog at https://www.the100.ci/ *Links* Papers - Rohrer, J. (2024) "Causal inference for psychologists who think that causal inference is not for them" ( https://compass.onlinelibrary.wiley.com/doi/10.1111/spc3.12948 ) - Bailey, D., ..., Rohrer, J. et al (2024) "Causal inference on human behaviour" ( https://www.nature.com/articles/s41562-024-01939-z.epdf ) - Rohrer, J. et al (2024) "The Effects of Satisfaction with Different Domains of Life on General Life Satisfaction Vary Between Individuals (But We Cannot Tell You Why)" ( https://doi.org/10.1525/collabra.121238 ) - Rohrer et al (2017) "Probing Birth-Order Effects on Narrow Traits Using Specification-Curve Analysis" ( https://journals.sagepub.com/doi/abs/10.1177/0956797617723726 ) Books - Watts, D. (2012) "Everything Is Obvious: How Common Sense Fails Us" ( https://amzn.to/3Z384V6 ) - Kucharski, A. (2025) "Proof: The Art and Science of Certainty" ( https://amzn.to/3HcIp6l ) Support the show Causal Bandits Podcast Causal AI || Causal Machine Learning || Causal Inference & Discovery Web: https://causalbanditspodcast.com Connect on LinkedIn: https://www.linkedin.com/in/aleksandermolak/ Join Causal Python Weekly: https://causalpython.io The Causal Book: https://amzn.to/3QhsRz4

April 14, 2025Episode 41 hr 10 min

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 ecosystem might bring some answers. Will they—combined with new methods for validating causal models now available in DoWhy—impact the way we build and interact with causal models in industry? ------------------------------------------------------------------------------------------------------ Video version available on Youtube: https://youtu.be/8yWKQqNFrmY Recorded on Mar 12, 2025 in Bengaluru, India. ------------------------------------------------------------------------------------------------------ *About The Guest* Amit Sharma is a Principal Researcher at Microsoft Research and one of the original creators of the open-source Python library DoWhy, considered the "scikit-learn of causal inference." He holds a PhD in Computer Science from Cornell University. His research focuses on causality and its intersection with LLM-based and agentic systems. Amit deeply cares about the social impact of machine learning systems and sees causality as one of the main drivers of more useful and robust systems. Connect with Amit: - Amit on LinkedIn: https://www.linkedin.com/in/amitshar/ - Amit on BlueSky: - Amit 's web page: http://amitsharma.in/ *About The Host* Aleksander (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-molak Support the show Causal Bandits Podcast Causal AI || Causal Machine Learning || Causal Inference & Discovery Web: https://causalbanditspodcast.com Connect on LinkedIn: https://www.linkedin.com/in/aleksandermolak/ Join Causal Python Weekly: https://causalpython.io The Causal Book: https://amzn.to/3QhsRz4