Causal Bandits Podcast

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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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✅  Recommended Playlists

👉 Causal Bandits Podcast
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👉 Causal Bandits Podcast Shorts
   • Answer with Causal Identification #po..

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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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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 

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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, 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

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!

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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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 *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

Send us a text

 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 

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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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

Send us a text

 What makes two tech giants collaborate on an open source causal AI package?

Emre's adventure with causal inference and causal AI has started before it was trendy.

He's one of the original core developers of DoWhy - one of the most popular and powerful Python libraries for causal inference - and a researcher focused on the intersection of causal inference, causal discovery, generative modeling and social impact.

His unique perspective, inspired by his experience with low-level programming combined with his vivid interest in how humans interact with technology, is driven by a deep seated desire to solve problems that matter to people.

In the episode we discuss:
🔹 What makes Microsoft and Amazon collaborate on an open source Python package?
🔹 Causal AI and the core of science
🔹 Is language model a world model?
🔹 When modeling physics is useful?

Ready to dive in?

Join the insightful discussions at https://causalbanditspodcast.com/

About The Guest
Emre Kıcıman, PhD is a Senior Principal Research Manager at Microsoft Research. He's one of the core developers of the DoWhy Python package, alongside Amit Sharma. He holds a PhD in computer science from Stanford University. Privately, he loves to climb and spend time with his family.

Connect with Emre:
- Emre on Twitter/X
- Emre on LinkedIn
- Emre'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
Libraries
- DoWhy (https://www.pywhy.org/dowhy/v0.11.1/)
- EconML (https://econml.azurewebsites.net/)
- CausalPy (https://causalpy.readthedocs.io/en/latest/)

Books
- Molak, A. - "Causal Inference and Discovery in Python"
- Pearl, J. - "Causality"

Causal Bandits Team
Project Coordinator:

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

Recorded on Jan 17, 2024 in London, UK.

Video version available here

What makes so many predictions about the future of AI wrong?

And what's possible with the current paradigm?

From medical imaging to song recommendations, the association-based paradigm of learning can be helpful, but is not sufficient to answer our most interesting questions.

Meet Athanasios (Thanos) Vlontzos who looks for inspirations everywhere around him to build causal machine learning and causal inference systems at Spotify's Advanced Causal Inference Lab.

In the episode we discuss:
- Why is causal discovery a better riddle than causal inference?
- Will radiologists be replaced by AI in 2024 or 2025?
- What are causal AI skeptics missing?
- Can causality emerge in Euclidean latent space?

Ready to dive in?

About The Guest
Athanasios (Thanos) Vlontzos, PhD is a Research Scientist at Advanced Causal Inference Lab at Spotify. Previousl;y, he worked at Apple, at SETI Institute with NASA stakeholders and published in some of the best scientific journals, including Nature Machine Learning. He's specialized in causal modeling, causal inferernce, causal discovery and medical imaging.

Connect with Athanasios:
- Athanasios on Twitter/X
- Athanasios on LinkedIn
- Athanasios'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
The full list of links 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

Send us a text

Video version available here

Are markets efficient, and if not, can causal models help us leverage the inefficiencies?

Do we really need to understand what we're modeling?

What's the role of symmetry in modeling financial markets?

What are the main challenges in applying causal models in finance?

Ready to dive in?


About The Guest
Alexander Denev is the CEO of Turnleaf Analytics. He's an author of multiple books on financial modeling and a former Head of AI (Financial Services) at Deloitte. He lectures at the University of Oxford and has worked for organizations like IHS Markit, The Royal Bank of Scotland (RBS), and the European Investment Bank. He has over 20 years of experience in finance, data science, and modeling. His first book about causal models was published well ahead of its time.

Connect with Alexander:
- Alexander on LinkedIn
- Alexander'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

Full list of links can be found here.

#machinelearning #causalai #causalinference #causality #finance #CauslBanditsPodcast

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

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Video version of this episode is available here

Causal Inference with LLMs and Reinforcement Learning Agents?

Do LLMs have a world model?

Can they reason causally?

What's the connection between LLMs, reinforcement learning, and causality?

Andrew Lampinen, PhD (Google DeepMind) shares the insights from his research on LLMs, reinforcement learning, causal inference and generalizable agents.

We also discuss the nature of intelligence, rationality and how they play with evolutionary fitness.

Join us in the journey!

Recorded on Dec 1, 2023 in London, UK.

About The Guest
Andrew Lampinen, PhD is a Senior Research Scientist at Google DeepMind. He holds a PhD in PhD in Cognitive Psychology from Stanford University. He's interested in cognitive flexibility and generalization, and how these abilities are enabled by factors like language, memory, and embodiment.

Connect with Andrew:
- Andrew on Twitter/X
- Andrew'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 (https://amzn.to/3QhsRz4).

Connect with Alex:
- Alex on the Internet

Links
Papers
- Lampinen et al. (2023) - "Passive learning of active causal strategies in agents and language models" (https://arxiv.org/pdf/2305.16183.pdf)

- Dasgupta, Lampinen, et al. (2022) Language models show human-like content effects on reasoning tasks" (https://arxiv.org/abs/2207.07051)

- Santoro, Lampinen, et al. (2021) - "Symbolic behaviour in artificial intelligence" (https://www.researchgate.net/publication/349125191_Symbolic_Behaviour_in_Artificial_Intelligence)

- Webb et al. (2022) - “Emergent Analogical Reasoning in Large Language Models” (https://arxiv.org/abs/2212.09196) 

Books
- Tomasello (2019) - “Becoming Human” (ht

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

Support the show

Video version available on YouTube

Do We Need Probability?

Causal inference lies at the very heart of the scientific method.

Randomized controlled trials (RCTs; also known as randomized experiemnts or A/B tests) are often called "the golden standard for causal inference".

It's a less known fact that randomized trials have their limitations in answering causal questions.

What are the most common myths about randomization?

What causal questions can and cannot be answered with randomized experiments? Finally, why do we need probability?

Join me on a fascinating journey into clinical trials, randomization and generalization.

Ready to meet Stephen Senn?

About The Guest
Stephen Senn, PhD, is a statistician and consultant specializing in clinical trials for drug development. He is a former Group Head at Ciba-Geigy and has served as a professor 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 Twitter/X
- Stephen on LinkedIn
- Stephen'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
Find the links here

Causal Bandits Team
Project Coordinator: Taiba Malik
Video and Audio Editing: Navneet Sharma, Aleksander Molak

#causalai #causalinference #causality #abtest #statistics #experiements

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

Support the show

Video version available on YouTube
Recorded on Nov 12, 2023 in Undisclosed location, Undisclosed location


From Systems Biology to Causality

Robert always loved statistics.

He went to study systems biology, driven by his desire to model natural systems.

His perspective on causal inference encompasses graphical models, Bayesian inference, reinforcement learning, generative AI and cognitive science.

It allows him to think broadly about the problems we encounter in modern AI research.

Is the reward enough and what's the next big thing in causal (generative) AI?

Let's see!

About The Guest
Robert Osazuwa Ness is a Senior Researcher at Microsoft Research. He explores how to combine causal discovery, causal inference, deep probabilistic modeling, and programming languages in search of new capabilities for AI systems.

Connect with Robert:
- Robert on Twitter/X
- Robert on LinkedIn
- Robert'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
Find the links here

Causal Bandits Team
Project Coordinator: Taiba Malik
Video and Audio Editing: Navneet Sharma, Aleksander Molak

#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 a text

Support the show

Video version available on YouTube
Recorded on Sep 27, 2023 in München, Germany


From supply chain to large language models and back

Ishansh realized the potential of data when he was just 10 years old, during his time as a junior cricket player.

His journey led him to ask questions about the mechanisms behind the observed events.

 Can large language models (LLMs) help in building an industrial causal graph?

What inspires stakeholders to share their knowledge and which causal discovery algorithms have been most effective for Ishansh's supply chain use case?

Hear the insights from one of the BMW Group's fastest-rising young data science talents.

Ready?

About The Guest
Ishansh Gupta is a Lead Data Scientist at BMW Group. Previously, he worked for several companies, including a legendary German sports club SV Werder Bremen. He studied Computer Science, and co-founded an educational startup during his study years. He has supervised or supported students in various universities, including the Munich-based TUM and MIT.

Connect with Ishansh:
- Ishansh on Twitter/X
- Ishansh 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

Connect with Alex:
- Alex on the Internet

Links
Papers
Full list of papers here

Books
- Molak (2023) - Causal Inference and Discovery in Python
- Pearl & Mackenzie (2019) - The Book of Why

Other
- causaLens

Causal Bandits Team
Project Coordinator: Taiba Malik
Video and Audio Editing: Navneet Sharma, Aleksander

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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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Video version of this episode is available on YouTube
Recorded on Oct 15, 2023 in São Paulo, Brazil


Causal Inference in Fintech? For Brave and True Only

From rural Brazil to one of the country’s largest banks, Matheus’ journey could inspire many.

Similarly to our previous guest, Iyar Lin, Matheus was interested in politics, but switched to economics, where he fell in love with math.

Observing the state of the industry, he quickly realized that without causality, we cannot answer some of the most interesting business questions.

His popular online book 'Causal Inference for The Brave and True' was a side effect of his strong drive to learn causal inference and causal machine learning, while collecting as much feedback as possible along the way.

Did he succeed?

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About The Guest
Matheus Facure is a Staff Data Scientist at Nubank and the author of "Causal Inference for The Brave and True" and "Causal Inference in Python".

Connect with Matheus:
- Matheus on Twitter/X
- Matheus on LinkedIn
- Matheus'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
Books
- Facure (2023) – Causal Inference in Python
- Molak (2023) – Causal Inference and Discovery in Python


Webcasts
- AMA Webcasts

Causal Bandits Team
Project Coordinator: Taiba Malik 
Video and Audio Editing: Navneet Sharma, Aleksa

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

Support the show

Video version available on YouTube
Recorded on Sep 13, 2023 in Beit El'Azari, Israel


The eternal dance between the data and the model

Early in his career, Iyar realized that purely associative models cannot provide him with the answers to the questions he found most interesting.

This realization laid the groundwork for his search for methods that go beyond statistical summaries of the data.

What started as a lonely journey, led him to become a data science lead at his current company, where he fosters causal culture daily.

Iyar developed a framework that helps digital product companies make better decisions regarding their products at scale and at budget.

Here, causality is not just a concept, but a tool for change.

Ready to dive in?

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About The Guest
Iyar Lin is a Data Science Lead at Loops, where he helps customers make better decisions leveraging causal inference and machine learning methods. He holds master's degree in statistics from The Hebrew University of Jerusalem. Before Loops, he worked at ViaSat and SimilarWeb.

Connect with Iyar:
- Iyar on LinkedIn
- Iyar'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 (https://amzn.to/3QhsRz4).

Connect with Alex:
- Alex on the Internet

Links
Papers
- Breiman (2001) - Statistical Modeling: The Two Cultures

Books
- Molak (2023) - Causal Inference and Discovery in Python
- Pearl et al. (2016) - Causal Inference in Statistics - A Pri

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

Support the show

Video version available on YouTube
Recorded on Sep 4, 2023 in London, UK


A causal bet
Darko's story begins in Eastern Europe, where his early attempts in building a business and the influence of early-stage role models shaped his attitudes and helped him move through challenging and lonely moments in his career.

See how mosquitos, Pascal programming language, and problems with generalization in vision models inspired Darko to build a company that helps some of the world's top companies streamline and deploy causal inference workflows today.

Learn how his hedge fund experience shaped his thinking about business.

Causal Bandits Extra is a series of conversations with non-technically-focused people involved in or interested in causality from business, social and other perspectives.

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About The Guest
Darko Matovski, PhD is the co-founder and CEO of causaLens, a $50M venture-backed scaleup. He holds a PhD in Computer Science and an MBA from the University of Southampton.

Connect with Darko:
- Darko Matovski on LinkedIn: https://www.linkedin.com/in/matovski/
- causaLens 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 causal machine learning.

Connect with Alex:
- Alex on the Internet

Causal Bandits Team
Project Coordinator: Taiba Malik (https://www.instagram.com/taibasplay/) Video and Audio Editing: Navneet Sharma, Aleksander Molak *Action* Connect on LinkedIn: https://www.linkedin.com/in/aleksandermolak/ Join Causal Python Weekly: https://causalpython.io Causal Bandits: https://causalbanditspodcast.com The Causal Book: https://amzn.to/3QhsRz4 *Sponsorship Disclaimer* This episode has been made possible with the support of causaLens. We appreciate their contribution to making

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