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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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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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https://www.youtube.com/@CausalPython/?sub_confirmation=1 

✅  Stay Connected With Me.

👉Twitter (X): https://twitter.com/AleksanderMolak  
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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, making them accessible whether you’re starting or advancing your knowledge. Here, I explore the intersections of causality, AI, machine learning, optimization, and decision-making, all through Python’s versatile capabilities.

This is also the home of The Causal Bandits Podcast. For more insightful discussions, check out my Causal Bandit Podcast Website.

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

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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!

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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
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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

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 Causal Bandits at AAAI 2024 || Part 1

In this special episode we interview researchers who presented their work at AAAI 2024 in Vancouver, Canada and participants of our workshop on causality and large language models (LLMs)

Time codes:
00:00 Intro
00:20 Osman Ali Mian (CISPA) - Adaptive causal discovery for time series
04:35 Emily McMilin (Independent/Meta) - LLMs, causality & selection bias
07:36 Scott Mueller (UCLA) - Causality for EV incentives
12:41 Andrew Lampinen (Google DeepMind) - Causality from passive data
15:16 Ali Edalati (Huawei) - About Causal Parrots workshop
15:26 Adbelrahman Zayed (MILA) - About Causal Parrots workshop 

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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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Meet The Godfather of Modern Causal Inference

His work has pretty literally changed the course of my life and I am honored and incredibly grateful we could meet for this great conversation in his home in Los Angeles

To anybody who knows something about modern causal inference, he needs no introduction.

He loves history, philosophy and music, and I believe it's fair to say that he's the godfather of modern causality.

Ladies & gentlemen, please welcome, professor Judea Pearl.

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About The Guest
Judea Pearl is a computer scientist, and a creator of the Structural Causal Model (SCM) framework for causal inference. In 2011, he has been awarded the Turing Award, the highest distinction in computer science, for his pioneering works on Bayesian networks and graphical causal models and "fundamental contributions to artificial intelligence through the development of a calculus for probabilistic and causal reasoning".

Connect with Judea:

• Judea on Twitter/X
• Judea's webpage

About The Host
Aleksander (Alex) Molak is an independent machine learning researcher, educator, entrepreneur and a best-selling author in the area of causality.

Connect with Alex:

• Alex on the Internet 

Links

• Pearl, J. - "The Book of Why"
• Kahneman, D. - "Thinking, Fast and Slow"
• Molak, A. - "Causal Inference & Discovery in Python"

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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Can we say something about YOUR personal treatment effect?

The estimation of individual treatment effects is the Holy Grail of personalized medicine.

It's also extremely difficult.

Yet, Scott is not discouraged from studying this topic.

In fact, he quit a pretty successful business to study it.

In a series of papers, Scott describes how combining experimental and observational data can help us understand individual causal effects.

Although this sounds enigmatic to many, the intuition behind this mechanism is simpler than you might think.

In the episode we discuss:

🔹 What made Scott quit a successful business he founded and study causal inference?
🔹 How a false conviction about his own skills helped him learn? 🔹 What are individual treatment effects?
🔹 Can we really say something about individual treatment effects?

Ready to dive in?

About The Guest
Scott Mueller is a researcher and a PhD candidate in causal modeling at UCLA, supervised by Prof. Judea Pearl. He's a serial entrepreneur and the founder of UCode, a coding school for kids. His current research focuses on the estimation of individual treatment effects and their bounds. He works under the supervision of professor Judea Pearl.

Connect with Scott:
- Scott on Twitter/X
- Scott's webpage


About The Host
Aleksander (Alex) Molak is an independent machine learning researcher, educator, entrepreneur and a best-selling author in the area of causality.

Connect with Alex:
- Alex on the Internet

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

Video version of this episode is available here

Causal personalization?
Dima did not love computers enough to forget about his passion for understanding people.

His work at Booking.com focuses on recommender systems and personalization, and their intersection with AB testing, constrained optimization and causal inference.

Dima's passion for building things started early in his childhood and continues up to this day, but recent events in his life also bring new opportunities to learn.

In the episode, we discuss:

• What can we learn about human psychology from building causal recommender systems?
• What it's like to work in a culture of radical experimentation?
• Why you should not skip your operations research classes?

Ready to dive in?

About The Guest
Dima Goldenberg is a Senior Machine Learning Manager at Booking.com, Tel Aviv, where he leads machine learning efforts in recommendations and personalization utilizing uplift modeling. Dima obtained his MSc in Tel Aviv University and currently pursuing PhD on causal personalization at Ben Gurion University of the Negev. He led multiple conference workshops and tutorials on causality and personalization and his research was published in top journals and conferences including WWW, CIKM, WSDM, SIGIR, KDD and RecSys.

Connect with Dima: 

• Dima on LinkedIn

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


Links
The full list of links is available here

#machinelearning #causalai #causalinference #causality 

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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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Was Deep Learning Revolution Bad For Causal Inference?

Did deep learning revolution slowed down the progress in causal research?

Can causality help in finding drug repurposing candidates?

What are the main challenges in using causal inference at scale?

Ehud Karavani, the author of the CausalLib Python library and Researcher at IBM Research shares his experiences and thoughts on these challenging questions.

Ehud believes in the power of good code, but for him code is not only about software development.

He sees coding as an inseparable part of modern-day research.

A powerful conversation for anyone interested in applied causal modeling.

In this episode we discuss:

• Can causality help in finding drug repurposing candidates?
• Challenges in data processing for causal inference at scale
• Motivation behind Python causal inference library CausalLib
• Working at IBM Research Ready to dive in? 

About The Guest
Ehud Karavani, MSc is Research Staff Member at IBM Research in the Causal Machine Learning for Healthcare & Life Sciences Group. He focuses on high-throughput causal inference for finding new indications for existing drugs using electronic health records and insurance claims data. He's the original author of Causallib - one of the first Python libraries specialized in causal inference.

Connect with Ehud:

• Ehud on Twitter/X
• Ehud on LinkedIn
• Ehud's web page 

About The Host
Aleksander (Alex) Molak is an independent machine learning researcher, educator, entrepreneur and a best-selling author in the area of causality.

Connect with Alex: Alex on the Internet

Links
Links for this episode can be found here

Video version of this episode can be found here. 

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 AI: The Melting Pot. Can Physics, Math & Biology Help Us?

What is the relationship between physics and causal models?

What can science of non-human animal behavior teach causal AI researchers?

Bernhard Schölkopf's rich background and experience allow him to combine perspectives from computation, physics, mathematics, biology, theory of evolution, psychology and ethology to build a deep understanding of underlying principles that govern complex systems and intelligent behavior.

His pioneering work in causal machine learning has revolutionized the field, providing new insights that enhance our ability to understand causal relationships and mechanisms in both natural and artificial systems.

In the episode we discuss:

• Does evolution favor causal inference over correlation-based learning?
• Can differential equations help us generalize structural causal models?
• What new book is Bernhard working on?
• Can ethology inspire causal AI researchers?

Ready to dive in?


About The Guest
Bernhard Schölkopf, PhD is a Director at Max Planck Institute for Intelligent Systems. He's one of the cofounders of European Lab for Learning & Intelligent Systems (ELLIS) and a recepient of the ACM Allen Newell Award, BBVA Foundation Frontiers of Knowledge Award, and more. His contributions to modern machine learning are hard to overestimate. He's a an affiliated professor at ETH Zürich, honorary professor at the University of Tübingen and the Technical University Berlin. His pioneering work on causal inference and causal machine learning inspired thousands of researchers and practitioners worldwide. 

Connect with Bernhard:

• Bernhard on Twitter/X
• Bernhard on LinkedIn
• Bernhard's web page

About The Host
Aleksander (Alex) Molak is an independent machine learning researcher, educator, entrepreneur and a best-selling author in the area of causality. 

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