Causal Bandits Podcast

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

Causal Bandits Podcast is a technology podcast hosted by Alex Molak, with 40 episodes on record and a Required Pod Score of 80. PitchCentric scores this show on Booking Probability, Listen Score, and live audience signals refreshed every 24 hours.

Alex Molak hosts Causal Bandits Podcast, a technology show with 40 episodes published.

Our AI reads these to draft pitches. Use them as grounding for a pitch that cites a real guest and a specific topic.

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  Episode #12

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

  Jul 13, 202656 minS2

  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

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  Episode #11

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

  Jun 1, 202643 minS2

  <p>Send us Fan Mail</p><p>*How do you forecast an event that has never happened before?*<br/><br/>How do you forecast an event that has never happened before?<br/><br/>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.<br/><br/>In this episode, we cover:<br/><br/>- Why value-at-risk and other correlation-based models break exactly when you...

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  Episode #10

  Causality, Experimentation, and Marketplaces | Lawrence De Geest S2E10

  Apr 1, 20261h 5mS2

  <p>Send us Fan Mail</p><p><b>Causality, Experimentation, and Marketplaces<br/></b><br/>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.<br/><br/>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.<br/><br/>- Why simple A/B tests fail at marketplaces<br/>- How reversing synthetic controls logic can help us...

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  Episode #9

  Do Heterogeneous Treatment Effects Exist? | Stephen Senn X Richard Hahn S2E9 | CausalBanditsPodcast

  Jan 30, 20261h 8mS2

  <p>Send us Fan Mail</p><p><b>Do Heterogeneous Treatment Effects Exist?<br/></b><br/>For the last 50 years, we've designed cars to be safe...<br/><br/>For the 50th-percentile male.<br/><br/>Well, that's actually not 100% correct.<br/><br/>According to Stanford's report, we introduced "female" crash test dummies in the 1960s, but...<br/><br/>They were just scaled-down versions of male dummies and...<br/><br/>Represented the 5th percentile of females in terms of body size and mass (aka the smallest 5% of women in the general population).<br/><br/>These dummies also did not take into account...

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  Episode #8

  Causal Inference & the "Bayesian-Frequentist War" | Richard Hahn S2E8 | CausalBanditsPodcast.com

  Dec 27, 20251h 24mS2

  <p>Send us Fan Mail</p><p>*What can we learn about causal inference from the “war” between Bayesians and frequentists?*<br/><br/>What can we learn about causal inference from the “war” between Bayesians and frequentists?<br/><br/>In the episode, we cover:<br/><br/>- What can we learn from the “war” between Bayesians and frequentists?<br/>- Why do Bayesian Additive Regression Trees (BART) “just work”?<br/>- Do heterogeneous treatment effects exist?<br/>- Is RCT generalization a heterogeneity problem?<br/><br/>In the episode, we accidentally coined a new term: “feature-level selection bias.”<br/><br/>...

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