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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

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