Send us a textCausal 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)=============================🔔Unlock the power of Python in AI and machine learning. Subscribe for simple insights into Causal Inference and Discovery.   / @causalpython   ✅ Important Links to Follow🔗Medium Blog  / aleksander-molak   🔗Newsletter Webhttps://causalpython.io/ 🔗 Linkshttps://bit.ly/m/alex-bio 🔗 GitHubhttps://github.com/AlxndrMlk ✅  Stay Connected With Me.👉Twitter (X):   / aleksandermolak    👉Linkedin:   / aleksandermolak   👉Facebook:   / causalpython    👉Instagram:   / alex.molak   👉Tiktok:   / alex.molak   👉Causal Bandits Podcast Website: https://causalbanditspodcast.com/✅ For Business Inquiries:  hello@causalpython.io =============================✅  Recommended Playlists👉 Causal Bandits Podcast   • Matej Zečević On Causality In AI: Can...   👉 Causal Bandits Podcast Shorts   • Answer with Causal Identification #po..=================================© Causal Python with Alex MolakSupport the showCausal Bandits PodcastCausal AI || Causal Machine Learning || Causal Inference & DiscoveryWeb: https://causalbanditspodcast.comConnect 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 textWhich 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 Discovery08:52 Oliver Schacht - Hyperparameter Tuning for DML14:41 Yanai Elazar - Causal Effect of Early ArXiving on Paper Acceptance18:53 Simon Bing - Identifying Linearly-Mixed Causal Representations from Multi-Node Interventions=============================🔔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/ 👉Facebook: https://www.facebook.com/CausalPython  👉Instagram: https://www.instagram.com/alex.molak/ 👉Tiktok: https://www.tiktok.com/@alex.molak 👉Causal Bandits Podcast Website: https://causalbanditspodcast.com/✅ For Business Inquiries:  hello@causalpython.io =============================✅  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.============Support the showCausal Bandits PodcastCausal AI || Causal Machine Learning || Causal Inference & DiscoveryWeb: https://causalbanditspodcast.comConnect 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 textRoot 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 Debeire2:41 - 06:37    Yuchen Zhu06:37 - 10:09   Konstantin Göbler10:09 - 17:05   Urja Pawar17:05 - 23:16  William OrchardEnjoy!Support the showCausal Bandits PodcastCausal AI || Causal Machine Learning || Causal Inference & DiscoveryWeb: https://causalbanditspodcast.comConnect 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 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) - Transportability4:19 - 9:53 Patrick Altmeyer (Delft) - Explainability & black-box models9:54 - 12:24 Lokesh Nagalapatti (IIT Bombay) - Continuous treatment effects12:24 - 16:06 Golnoosh Farnadi (McGill University) - Causality & responsible AI16:06 - 17:37 Markus Bläser (Saarland University) - Fast identification of causal parameters17:37 - 22:37 Devendra Singh Dhami (TU/e) - The future of causal AI Support the showCausal Bandits PodcastCausal AI || Causal Machine Learning || Causal Inference & DiscoveryWeb: https://causalbanditspodcast.comConnect 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 1In 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 Intro00:20 Osman Ali Mian (CISPA) - Adaptive causal discovery for time series04:35 Emily McMilin (Independent/Meta) - LLMs, causality & selection bias07:36 Scott Mueller (UCLA) - Causality for EV incentives12:41 Andrew Lampinen (Google DeepMind) - Causality from passive data15:16 Ali Edalati (Huawei) - About Causal Parrots workshop15:26 Adbelrahman Zayed (MILA) - About Causal Parrots workshop Support the showCausal Bandits PodcastCausal AI || Causal Machine Learning || Causal Inference & DiscoveryWeb: https://causalbanditspodcast.comConnect on LinkedIn: https://www.linkedin.com/in/aleksandermolak/Join Causal Python Weekly: https://causalpython.io The Causal Book: https://amzn.to/3QhsRz4

In this conversation, Judea Pearl, the godfather of modern causal inference, dives into the complexities of causal reasoning and artificial intelligence. He discusses the influence of human biases on AI's decision-making and the potential future of large language models in scientific experimentation. Pearl emphasizes the importance of bridging causal reasoning with statistical education, using malaria as a case study. He also shares insights on the challenges of implementing evidence-based frameworks in education, advocating for a unified understanding of causality across disciplines.

Send us a textCan 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 GuestScott 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 webpageAbout The HostAleksander (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 InternetSupport the showCausal Bandits PodcastCausal AI || Causal Machine Learning || Causal Inference & DiscoveryWeb: https://causalbanditspodcast.comConnect on LinkedIn: https://www.linkedin.com/in/aleksandermolak/Join Causal Python Weekly: https://causalpython.io The Causal Book: https://amzn.to/3QhsRz4

Dima Goldenberg from Booking.com discusses building causal recommender systems, radical experimentation culture, and the importance of operations research classes. Topics include human psychology insights, optimizing promotions, dealing with noise in data, and childhood game lessons in work strategies.

Send us a textWas 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 scaleMotivation behind Python causal inference library CausalLibWorking at IBM Research Ready to dive in? About The GuestEhud 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/XEhud on LinkedInEhud's web page About The HostAleksander (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 InternetLinksLinks for this episode can be found here Video version of this episode can be found here. Support the showCausal Bandits PodcastCausal AI || Causal Machine Learning || Causal Inference & DiscoveryWeb: https://causalbanditspodcast.comConnect 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 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 GuestBernhard 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/XBernhard on LinkedInBernhard's web pageAbout The HostAleksander (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 showCausal Bandits PodcastCausal AI || Causal Machine Learning || Causal Inference & DiscoveryWeb: https://causalbanditspodcast.comConnect on LinkedIn: https://www.linkedin.com/in/aleksandermolak/Join Causal Python Weekly: https://causalpython.io The Causal Book: https://amzn.to/3QhsRz4