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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

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Causal Bandits Podcast
Causal AI || Causal Machine Learning || Causal Inference & Discovery
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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_Arti

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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

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Causal AI || Causal Machine Learning || Causal Inference & Discovery
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The Causal Book: https://amzn.to/3QhsRz4

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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 

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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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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

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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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) -

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 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

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Video version of this episode is available here
Recorded on Sep 5, 2023 in Oxford, UK


Have you ever wondered if we can answer seemingly unanswerable questions?

Jakob's journey into causality started when he was 12 years old.

Deeply dissatisfied with what adults had to offer when asked about the sources of causal knowledge, he started to look for the answers on his own.

He studied philosophy, politics and economics to find his place at UCL's Centre for Artificial Intelligence, where he met his future PhD advisor, Prof. Ricardo Silva.

At the center of Jakob's interests lies decision-making under partial knowledge.

He's passionate about partial identification, sensitivity analysis, and optimal experiments, yet he's far from being just a theoretician.

He implements causal ideas he finds promising in the context of material discovery at Matterhorn Studio, earlier he worked on sensitivity analysis for quasi-experimental methods at Spotify.

Want to learn what a 1000-years-old church, communism and Justin Bieber have to do with causality?

Tune in! ------------------------------------------------------------------------------------------------------

About The Guest
Jakob Zeitler is a researcher at Centre for Artificial Intelligence at University College London (UCL) and a Head of R&D at Matterhorn Studio. His research focuses on partial identification, sensitivity analysis and optimal experimentation. He works on solutions for automated material design.

Connect with Jakob:
- Jakob Zeitler on Twitter/X
- Jakob Zeitler on LinkedIn
- Jakob Zeitler'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

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 29, 2023 in Cambridge, UK


Should we continue to ask why?

Alicia's machine learning journey began with... causal machine learning.

Starting with econometrics, she discovered semi-parametric methods and the Pearlian framework at later stages of her career and incorporated both in her everyday toolkit.

She loves to understand why things work, which inspires her to ask "why" not only in the context of treatment effects, but also in the context of general machine learning. Her papers on heterogeneous treatment effect estimators and model evaluation bring unique perspectives to the community.

Her recent NeurIPS paper on double descent aims at bridging the gap between statistical learning theory and a counter-intuitive phenomenon of double descent observed in complex machine learning architectures.

Ready to dive in? ------------------------------------------------------------------------------------------------------ About The Guest
Alicia Curth is a Machine Learning Researcher and a final year PhD student at The van der Schaar Lab at Cambridge University. Her research is focused on causality, understanding machine learning methods from ground up and personalized medicine. Her works are frequently accepted at best machine learning conferences (she's a true serial NeurIPS author).

Connect with Alicia:
- Alicia on Twitter/X
- Alicia on LinkedIn
- Alicia '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
See here  for the full list of links

Causal Bandits Team 
Project Coordinator: Taiba Mal

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