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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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Causal AI || Causal Machine Learning || Causal Inference & Discovery
Web: https://causalbanditspodcast.com

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Join Causal Python Weekly: https://causalpython.io
The Causal Book: https://amzn.to/3QhsRz4

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*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

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*Agents, Causal AI & The Future of DoWhy*

The idea of agentic systems taking over more complex human tasks is compelling.

New "production-grade" frameworks to build agentic systems pop up, suggesting that we're close to achieving full automation of these challenging multi-step tasks.

But is the underlying agentic technology itself ready for production?

And if not, can LLM-based systems help us making better decisions?

Recent new developments in the DoWhy/PyWhy ecosystem might bring some answers.

Will they—combined with new methods for validating causal models now available in DoWhy—impact the way we build and interact with causal models in industry?

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Video version available on Youtube: 

https://youtu.be/8yWKQqNFrmY

Recorded on Mar 12, 2025 in Bengaluru, India.

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*About The Guest*
Amit Sharma is a Principal Researcher at Microsoft Research and one of the original creators of the open-source Python library DoWhy, considered the "scikit-learn of causal inference." He holds a PhD in Computer Science from Cornell University. His research focuses on causality and its intersection with LLM-based and agentic systems. Amit deeply cares about the social impact of machine learning systems and sees causality as one of the main drivers of more useful and robust systems.

Connect with Amit:
- Amit on LinkedIn: https://www.linkedin.com/in/amitshar/
- Amit on BlueSky:
- Amit 's web page: http://amitsharma.in/

*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 a text

 📽️ FREE Online Course on Causality 

📕 Causal Inference & Discovery in Python

Causal Secrets of N=1 Experiments

Join me for a one of a kind conversation on the opportunities and challenges of n-of-1 trials, Eric's causal journey, his path into statistics, his love of sci-fi, and how single-subject experiments could reshape personalized medicine.

Video version available here

About The Guest

Dr. ​Eric J. Daza is a biostatistician and health data scientist with over 22 years of experience (Cornell, UNC Chapel Hill, Stanford). He works at Boehringer Ingelheim. Eric is a creator of Stats-of-1, a health innovation newsletter & podcast on n-of-1 trials, single-case designs, switchback experiments, and personal AI for digital health/medicine.

All views and opinions expressed by Dr. Eric J. Daza represent no one but himself. These views and opinions do not represent the views and opinions of his employer.

Connect with Eric:

• Eric on LinkedIn
• Eric on BlueSky
• Eric's web page

About The Host

Connect with Alex:

• Alex on the Internet 
•  👉🏼 Consulting and Causal AI Training For Your Team: hello causalpython.io

Episode Links

Papers

• Daza (2018) - "Causal Analysis of Self-tracked Time Series Data Using a Counterfactual Framework for N-of-1 Trials"
• Matias, Daza et al (2022) - "What possibly affects nighttime heart rate? Conclusions from N-of-1 observational data"

Books

• Asimov, I (1991) - "Foundation"

Apps

• StudyU

Webpages

• Stats-of-1

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

From Quantum Causal Models to Causal AI at Spotify

Ciarán loved Lego.

Fascinated by the endless possibilities offered by the blocks, he once asked his parents what he could do as an adult to keep building with them.

The answer: engineering.

As he delved deeper into engineering, Ciarán noticed that its rules relied on a deeper structure. This realization inspired him to pursue quantum physics, which eventually brought him face-to-face with fundamental questions about causality.

Today, Ciarán blends his deep understanding of physics and quantum causal models with applied work at Spotify, solving complex problems in innovative ways.

Recently, while collaborating with one of his students, he stumbled upon a new interesting question: could we learn something about the early history of the universe by applying causal inference methods in astrophysics?

Could we? Hear it from Ciarán himself.

Join us for this one-of-a-kind conversation!

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Video version and episode links available on YouTube
Recorded on Nov 6, 2024 in Dublin, Ireland.

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About The Guest
Ciarán Gilligan-Lee is Head of the Causal Inference Research Lab at Spotify and Honorary Associate Professor at University College London. He got interested in causality during his studies in quantum physics. This interest led him to study quantum causal models. He published in Nature Machine Intelligence, Nature Quantum Information, Physical Review Letters, New Journal of Physics and more. In his free time, he writes for New Scientist and helps his students apply causal methods in new fields (e.g., astrophysics).

Connect with Ciarán:
- Ciarán on LinkedIn: https://www.linkedin.com/in/ciaran-gilligan-lee/
- Ciarán's web page: https://www.ciarangilliganlee.com/

About The Host
Aleksander (Alex) Molak is an independent machine learning researcher, educator, entrepreneur

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

Stefan Feuerriegel is the Head of the Institute of AI in Management at LMU.

His team consistently publishes work on causal machine learning at top AI conferences, including NeurIPS, ICML, and more.

At the same time, they help businesses implement causal methods in practice.

They worked on projects with companies like ABB Hitachi, and Booking.com.

Stefan believes his team thrives because of its diversity and aims to bring more causal machine learning to medicine.

I had a great conversation with him, and I hope you'll enjoy it too!

>> Guest info:

Stefan Feuerriegel is a professor and the Head of the Institute of AI in Management at LMU. Previously, he worked as a consultant at McKinsey & Co. and ran his own AI startup.

>> Episode Links:

Papers

- Feuerriegel, S. et al. (2024) - Causal machine learning for predicting treatment outcomes (https://www.nature.com/articles/s41591-024-02902-1)

- Kuzmanivic, M. et al. (2024) - Causal Machine Learning for Cost-Effective Allocation of Development Aid (https://arxiv.org/abs/2401.16986)

- Schröder, M. et al. (2024) - Conformal Prediction for Causal Effects of Continuous Treatments (https://arxiv.org/abs/2407.03094)

>> WWW: https://www.som.lmu.de/ai/

>> LinkedIn: https://www.linkedin.com/in/stefan-feuerriegel/

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 Bandits at cAI 2024 (The Royal Society, London)

The cAI Conference in London slammed the door on baseless claims that causality cannot be used in industrial practice.

In the episode of Causal Bandits Extra we interview participants and speakers at Causal AI Conference London, who share their main insights from the event, and the challenges they face in applying causal methods in their everyday work.

Time codes:
00:29 - Eyal Kazin (Zimmer Biomet)
01:44 - Athanasios Vlontzos (Spotify)
04:02 - Mimie Liotsiou (Dunnhumby)
06:13 - Fernanda Hinze (Croud)
09:00 - Clara Higuera Cabañes (BBVA)
10:28 - Javier Moral Hernández (BBVA)
11:25 - Álvaro Ibraín Rodríguez (BBVA)
12:10 - Hugo Proença (Booking.com)
13:21 - Debora Andrade (Seamless AI)
15:09 - Puneeth Nikin (Croud)
17:54 - Puneet Gupta (Cisco)
19:43 - Arthur Mello (Sephora)


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Causal AI || Causal Machine Learning || Causal Inference & Discovery
Web: https://causalbanditspodcast.com

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Which models work best for causal discovery and double machine learning?

In this extra episode, we present 4 more conversations with the researchers presenting their work at the CLeaR 2024 conference in Los Angeles, California.

What you'll learn:
- Which causal discovery models perform best with their default hyperparameters?
- How to tune your double machine learning model?
- Does putting your paper on ArXiv early increase its chances of being accepted at a conference?
- How to deal with causal representation learning with multiple latent interventions?

Time codes:
00:24 Damian Machlanski - Hyperparameter Tuning for Causal Discovery
08:52 Oliver Schacht - Hyperparameter Tuning for DML
14:41 Yanai Elazar - Causal Effect of Early ArXiving on Paper Acceptance
18:53 Simon Bing - Identifying Linearly-Mixed Causal Representations from Multi-Node Interventions

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🔔Unlock the power of Python in AI and machine learning. Subscribe for simple insights into Causal Inference and Discovery.
https://www.youtube.com/@CausalPython/?sub_confirmation=1

✅  Stay Connected With Me.

👉Twitter (X): https://twitter.com/AleksanderMolak 
👉Linkedin: https://www.linkedin.com/in/aleksandermolak/
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✅ For Business Inquiries:  [email protected] 

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✅  About Causal Python with Alex Molak.

Welcome to my official YouTube channel, Causal Python, with Alex Molak. Dive into the fascinating world of Causal AI, unraveling the complexities of Causal Inference and Discovery with Python. 

My content simplifies these intricate topics,

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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Root cause analysis, model explanations, causal discovery.

Are we facing a missing benchmark problem?

Or not anymore?

In this special episode, we travel to Los Angeles to talk with researchers at the forefront of causal research, exploring their projects, key insights, and the challenges they face in their work.

Time codes:
0:15 - 02:40    Kevin Debeire
2:41 - 06:37    Yuchen Zhu
06:37 - 10:09   Konstantin Göbler
10:09 - 17:05   Urja Pawar
17:05 - 23:16  William Orchard

Enjoy!

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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 *Causal Bandits at AAAI 2024 || Part 2*

In this special episode we interview researchers who presented their work at AAAI 2024 in Vancouver, Canada.

Time codes:
 00:12 - 04:18 Kevin Xia (Columbia University) - Transportability
4:19 - 9:53 Patrick Altmeyer (Delft) - Explainability & black-box models
9:54 - 12:24 Lokesh Nagalapatti (IIT Bombay) - Continuous treatment effects
12:24 - 16:06 Golnoosh Farnadi (McGill University) - Causality & responsible AI
16:06 - 17:37 Markus Bläser (Saarland University) - Fast identification of causal parameters
17:37 - 22:37 Devendra Singh Dhami (TU/e) - The future of causal AI 

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