Causal Bandits Podcast

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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!

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Video version available on YouTube: https://youtu.be/O53Ra0iIFp8
Recorded on Apr 28, 2026 in New York, USA.

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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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*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."

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Video version available on the Youtube: https://youtu.be/FzKy2ws-7qs
Recorded on May 29, 2026 in London, UK.

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*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

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

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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

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.

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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: 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

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.”

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Video version available on the Youtube:
 https://youtu.be/-hRS8eU3Tow
Recorded in Arizona, US.

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*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