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Causal Bandits Podcast

Causal Bandits Podcast

De : Alex Molak
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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

© 2026 Causal Bandits Podcast
Economie Science
Épisodes
  • Causality, Experimentation, and Marketplaces | Lawrence De Geest S2E10
    Apr 1 2026

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    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

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    Video version available on YouTube: https://youtu.be/acCy16L33tU
    Recorded in 2026 in San Francisco, USA.

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    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

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    1 h et 5 min
  • Do Heterogeneous Treatment Effects Exist? | Stephen Senn X Richard Hahn S2E9 | CausalBanditsPodcast
    Jan 30 2026

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    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.

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    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:

    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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    1 h et 8 min
  • Causal Inference & the "Bayesian-Frequentist War" | Richard Hahn S2E8 | CausalBanditsPodcast.com
    Dec 27 2025

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    *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.”

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    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) - "StochT

    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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    1 h et 24 min
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