Causal Bandits Podcast

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 InternetInspiring Tech Leaders - The Technology PodcastInterviews with Tech Leaders and insights on the latest emerging technology trends.Listen on: Apple Podcasts   SpotifySupport 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 AInspiring Tech Leaders - The Technology PodcastInterviews with Tech Leaders and insights on the latest emerging technology trends.Listen on: Apple Podcasts   SpotifySupport 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

Bernhard Schölkopf, Director at the Max Planck Institute for Intelligent Systems, merges insights from physics, biology, and machine learning. He discusses how evolution might favor causal inference over mere correlation and the intricate ties between differential equations and causal models. Schölkopf emphasizes the importance of understanding biological intelligence to enhance AI development. Plus, he shares his exciting new book project, aiming to bridge gaps in causal inference and its application across disciplines.

Emre Kıcıman, a core developer of DoWhy, delves into open source causal AI with Microsoft and Amazon collaboration. They discuss the core of science in causal AI, the intersection of language models and world models, the usefulness of modeling physics, and the future of generative AI. The conversation explores challenges in causality, the importance of causal inference in decision-making, and the evolution of libraries promoting causal analysis.

Athanasios (Thanos) Vlontzos, a Research Scientist at Spotify's Advanced Causal Inference Lab, tackles intriguing questions about AI's future and causal modeling. He discusses why many AI predictions miss the mark and explores the evolving role of radiologists amid AI advancements. Thanos dives into challenges in medical AI, the humor of causal model pitfalls, and the essence of interdisciplinary collaboration. The conversation also highlights the connection between music and ideas, emphasizing the drive for exploration in science.

Send us a textVideo 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 GuestAlexander 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 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 InternetFull list of links can be found here.#machinelearning #causalai #causalinference #causality #finance #CauslBanditsPodcastInspiring Tech Leaders - The Technology PodcastInterviews with Tech Leaders and insights on the latest emerging technology trends.Listen on: Apple Podcasts   SpotifySupport 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 textLove Causal Bandits Podcast?Help us bring more quality content: Support the showVideo version of this episode is available hereCausal 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 GuestAndrew 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 HostAleksander (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 InternetLinksPapers- 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 reasoniInspiring Tech Leaders - The Technology PodcastInterviews with Tech Leaders and insights on the latest emerging technology trends.Listen on: Apple Podcasts   SpotifySupport 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

Guest Stephen Senn, an expert in causal inference and clinical trials, delves into the myths of randomization and the limitations of randomized trials in answering causal questions. He discusses the importance of understanding mechanisms of change, considering covariates, and the ethical challenges in clinical trials. Senn also explores innovative trial designs in asthma and chronic diseases, the impact of covariates in statistical analyses, and critiques the Bayesian method while engaging in philosophical discussions on determinism and free will.