Causal Bandits Podcast

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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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*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 (20
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 a text
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?

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Video version available on Youtube: 
https://youtu.be/Frb6eTW2ywk
Recorded on Aug 18, 2025 in Tübingen, Germany.

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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

Send us a text
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

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Video version available on the Youtube: https://youtu.be/qr5JolEAuJU
Recorded on Sep 16, 2025 in Berkeley, California, US.

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*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 Gen
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 a text
*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!

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Audio version available on YouTube: https://youtu.be/YQetmI-y5gM
Recorded on May 16, 2025, in Leipzig, Germany.

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*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
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