Causal Bandits Podcast

Send us a text*Agents, Causal AI & The Future of DoWhy*The idea of agentic systems taking over more complex human tasks is compelling.New "production-grade" frameworks to build agentic systems pop up, suggesting that we're close to achieving full automation of these challenging multi-step tasks.But is the underlying agentic technology itself ready for production?And if not, can LLM-based systems help us making better decisions?Recent new developments in the DoWhy/PyWhy ecosystem might bring some answers.Will they—combined with new methods for validating causal models now available in DoWhy—impact the way we build and interact with causal models in industry?------------------------------------------------------------------------------------------------------Video version available on Youtube: https://youtu.be/8yWKQqNFrmYRecorded on Mar 12, 2025 in Bengaluru, India.------------------------------------------------------------------------------------------------------*About The Guest*Amit Sharma is a Principal Researcher at Microsoft Research and one of the original creators of the open-source Python library DoWhy, considered the "scikit-learn of causal inference." He holds a PhD in Computer Science from Cornell University. His research focuses on causality and its intersection with LLM-based and agentic systems. Amit deeply cares about the social impact of machine learning systems and sees causality as one of the main drivers of more useful and robust systems.Connect with Amit:- Amit on LinkedIn: https://www.linkedin.com/in/amitshar/- Amit on BlueSky:- Amit 's web page: http://amitsharma.in/*About The Host*Everyday AI: Your daily guide to grown with Generative AICan't keep up with AI? We've got you. Everyday AI helps you keep up and get ahead.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

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Send us a textFrom Quantum Causal Models to Causal AI at SpotifyCiarán loved Lego.Fascinated by the endless possibilities offered by the blocks, he once asked his parents what he could do as an adult to keep building with them.The answer: engineering.As he delved deeper into engineering, Ciarán noticed that its rules relied on a deeper structure. This realization inspired him to pursue quantum physics, which eventually brought him face-to-face with fundamental questions about causality.Today, Ciarán blends his deep understanding of physics and quantum causal models with applied work at Spotify, solving complex problems in innovative ways.Recently, while collaborating with one of his students, he stumbled upon a new interesting question: could we learn something about the early history of the universe by applying causal inference methods in astrophysics?Could we? Hear it from Ciarán himself.Join us for this one-of-a-kind conversation!------------------------------------------------------------------------------------------------------Video version and episode links available on YouTubeRecorded on Nov 6, 2024 in Dublin, Ireland.------------------------------------------------------------------------------------------------------About The GuestCiarán Gilligan-Lee is Head of the Causal Inference Research Lab at Spotify and Honorary Associate Professor at University College London. He got interested in causality during his studies in quantum physics. This interest led him to study quantum causal models. He published in Nature Machine Intelligence, Nature Quantum Information, Physical Review Letters, New Journal of Physics and more. In his free time, he writes for New Scientist and helps his students apply causal methods in new fields (e.g., astrophysics).Connect with Ciarán:- Ciarán on LinkedIn: https://www.linkedin.com/in/ciaran-gilligan-lee/- Ciarán's web page: https://www.ciarangilliganlee.com/About The HostAleksander (Alex) Molak is an independent machine learning researcher, educator, entreSupport 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 textStefan Feuerriegel is the Head of the Institute of AI in Management at LMU.His team consistently publishes work on causal machine learning at top AI conferences, including NeurIPS, ICML, and more.At the same time, they help businesses implement causal methods in practice.They worked on projects with companies like ABB Hitachi, and Booking.com.Stefan believes his team thrives because of its diversity and aims to bring more causal machine learning to medicine.I had a great conversation with him, and I hope you'll enjoy it too!>> Guest info:Stefan Feuerriegel is a professor and the Head of the Institute of AI in Management at LMU. Previously, he worked as a consultant at McKinsey & Co. and ran his own AI startup.>> Episode Links:Papers- Feuerriegel, S. et al. (2024) - Causal machine learning for predicting treatment outcomes (https://www.nature.com/articles/s41591-024-02902-1)- Kuzmanivic, M. et al. (2024) - Causal Machine Learning for Cost-Effective Allocation of Development Aid (https://arxiv.org/abs/2401.16986)- Schröder, M. et al. (2024) - Conformal Prediction for Causal Effects of Continuous Treatments (https://arxiv.org/abs/2407.03094)>> WWW: https://www.som.lmu.de/ai/>> LinkedIn: https://www.linkedin.com/in/stefan-feuerriegel/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 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)=============================

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=============================

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

Send us a Text Message.Meet The Godfather of Modern Causal InferenceHis 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 AngelesTo 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.Subscribe to never miss an episodeAbout The GuestJudea 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/XJudea'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 Internet LinksPearl, J. - "The Book of Why"Kahneman, D. - "ThinkiShould we build the Causal Experts Network?Share your thoughts in the surveyAnything But LawDiscover inspiring stories and insights from entrepreneurs, athletes, and thought leaders.Listen on: Apple Podcasts SpotifySupport the Show.Causal 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 Message.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:

Send us a Text Message.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 GuestDima 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 LinkedInAbout 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 Internet LinksThe full list of links is available here#machinelearning #causalai #causalinference #causality Should we build the Causal Experts Network?Share your thoughts in the surveySupport the Show.Causal 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 Message.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 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 Show.Causal 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 Message. 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 All Business. No Boundaries.Welcome to All Business. No Boundaries, a collection of supply chain stories by DHL...Listen on: Apple Podcasts SpotifySupport the Show.Causal 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 Message. 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:

Recorded on Jan 17, 2024 in London, UK. Video version available hereWhat 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 GuestAthanasios (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 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 InternetLinksThe full list of links can be found here.Support the Show.Causal 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

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 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 #CauslBanditsPodcastClimate ConfidentWith a new episode every Wed morning, the Climate Confident podcast is weekly podcast...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

Love 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 reasoning tasks" (https://arxiv.org/abs/2207.07051)- Santoro, Lampinen, et al. (2021) - "Symbolic behaviour in artificial intelligeAll Business. No Boundaries.Welcome to All Business. No Boundaries., a collection of supply chain stories by DHL...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

Support the showVideo version available on YouTubeDo 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 GuestStephen 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 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 InternetLinksFind the links hereCausal Bandits TeamProject Coordinator: Taiba MalikThe Code of Entry PodcastThe Code of Entry Podcast, hosted by the insightful Greg Bew, delves deep into the...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

Support the showVideo version available on YouTube Recorded on Nov 12, 2023 in Undisclosed location, Undisclosed locationFrom Systems Biology to CausalityRobert 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 GuestRobert 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 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 InternetLinksFind the links hereCausal Bandits TeamProject Coordinator: Taiba MalikVideo and Audio Editing: Navneet Sharma, Aleksander Molak#causalai #causalinference #causality 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

Recorded on Sep 27, 2023 in München, GermanyVideo version available on YouTubeFrom supply chain to large language models and backIshansh 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 GuestIshansh 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 LinkedInAbout The HostAleksander (Alex) Molak is an independent machine learning researcher, educator, entrepreneur and a best-selling author in the area of causalityConnect with Alex: - Alex on the InternetLinksPapers - Kıcıman et al. (2023) - Causal Reasoning and Large Language Models - Peters et al. (2014) - Causal Discovery with Continuous Additive Noise Models [RESIT algorithm]Books- Molak (2023) - Causal Inference and Discovery in Python - Pearl & Mackenzie (2019) - The Book of WhyOther- causaLensCausal Bandits TeamProject Coordinator: Taiba MalikVideo and Audio EditinCausal 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

Recorded on Oct 15, 2023 in São Paulo, Brazil. Video version of this episode is available on YouTube.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? ------------------------------------------------------------------------------------------------------ About The GuestMatheus 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 pageAbout The HostAleksander (Alex) Molak is an independent machine learning researcher, educator, entrepreneur and a best-selling author in the area of causalityConnect with Alex: - Alex on the InternetLinksBooks - Facure (2023) – Causal Inference in Python- Molak (2023) – Causal Inference and Discovery in PythonWebcasts - AMA WebcastsCausal Bandits TeamProject Coordinator: Taiba Malik Video and Audio Editing: Navneet Sharma, Aleksander Molak #machinelearning #causalai #causalinference #causality #fintechCausal 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

 Recorded on Sep 13, 2023 in Beit El'Azari, IsraelVideo version available on YouTubeThe eternal dance between the data and the modelEarly 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?------------------------------------------------------------------------------------------------------ About The GuestIyar 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 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 - Breiman (2001) - Statistical Modeling: The Two CulturesBooks - Molak (2023) - Causal Inference and Discovery in Python- Pearl et al. (2016) - Causal Inference in Statistics - A PrimerCausal Bandits TeamProject Coordinator: Taiba Malik (Insta)Video and Audio Editing: Navneet Sharma, Aleksander Molak #machinelearning #causalai #causalinference #causality Causal 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

Recorded on Sep 4, 2023 in London, UK. Video version available on YouTubeA causal betDarko'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. ------------------------------------------------------------------------------------------------------ About The GuestDarko 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 pageAbout The HostAleksander (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 TeamProject 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 this content possible. While this episode is sponsored, rest assured that our views and discussions remain independent and genuine. Thank you for supporting the causal community. #machinelearning #causalai #causalinference #causality #startup #business #entrepreneurCausal 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

Recorded on Sep 5, 2023 in Oxford, UK. Video version of this episode is available here. 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 GuestJakob 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 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 LinksSee the full list of links here.Causal Bandits TeamProject Coordinator: Taiba MalikVideo and Audio Editing: Navneet Sharma, Aleksander MolakCausal 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

Recorded on Nov 29, 2023 in Cambridge, UK. Video version available on YouTube 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 GuestAlicia 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 InternetLinksSee here  for the full list of linksCausal Bandits Team Project Coordinator: Taiba MalikVideo and Audio Editing: Navneet Sharma, Aleksander Molak Causal 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

Recorded on Aug 29, 2023 in München, Germany. Video version available on YouTube.Can we meaningfully talk about causality in dynamical systems?Some people are puzzled when it comes to dynamical systems and the idea of causation.Dynamical systems well-known in physics, social sciences, and biology are often thought of as a special family of systems, where it might be difficult to meaningfully talk about causal direction. Naftali Weinberger devoted his career to examining the relationships between system dynamics, causality and the phenomena known broadly as "complexity". We explore what does "intervention" mean in a dynamical system and we deconstruct common intuitions about causality and system's equilibrium. We discuss the importance of time scales when defining a causal system, analyze what could have inspired Bertrand Russell to say that causality is a "relic of a bygone age" and ponder the phenomenon of emergence. Finally, Naftali shares his advice for those of us just starting exploring the uncharted territory of causal inference and discovery. Warning: this conversation might bend your sense of reality. Use with caution! Ready to dive in? About The GuestNaftali Weinberger, PhD is a Researcher at Munich Center for Mathematical Philosophy at LMU. His research is focused on causality, dynamical systems and fairness. He works with scientists, researchers and philosophers around the globe helping them address challenges in diverse fields like climate change, psychometrics, fairness and more. Connect with Naftali: Naftali on Twitter/XNaftali on BlueSky Naftali'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 InternetLinks are available hereCausal Bandits TeamProject Coordinator: Taiba MalikVideo and Audio Editing: Navneet SharmCausal 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

Recorded on Aug 27, 2023 in München, Germany. Video version available on YouTubeIs Causality Necessary For Autonomous Driving?From a child experimenter to a lead engineer working on a general causal inference engine, Daniel's choices have been marked by intense curiosity and the courage to take risks.Daniel shares how working with mathematicians differs from working with physicists and how having both on the team makes the team stronger. We discuss the journey Daniel and his team took to build a system that allows  performing the abduction step on a broad class of models in a computationally efficient way - a prerequisite to build a practically valuable counterfactual reasoning system.Finally, Daniel shares his experiences in communicating with stakeholders and offers  advice for those of us who only begin their journey with causality. Ready? About The GuestDaniel Ebenhöch is a Lead Engineer at e:fs Techhub. His research is focused on autonomous driving and automated decision-making. He leads a diverse team of scientists and developers, working on a general SCM-based causal inference engine. Connect with Daniel: - Daniel Ebenhöch on LinkedInAbout 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 InternetLinksPackages - PGMpy (https://pgmpy.org/) Books - Molak (2023) - Causal Inference and Discovery in Python- Pearl (2009) - Causality- Peters et al. (2017) - Elements of Causal Inference: Foundations and Learning AlgorithmsCausal Bandits TeamProject Coordinator: Taiba MalikVideo and Audio Editing: Navneet Sharma, Aleksander Molak Causal 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

Recorded on Aug 25, 2023 in Berlin, Germany.   Video version available on YouTube Is Marketing Intrinsically Causal? After spending 5 years talking to mathematicians, Juan decided to look for new opportunities that would offer him more immediate impact on the world. Little did he know that this journey will lead him to become a Senior Data Scientist at Wolt - one of the global food delivery leaders with operations in 25 countries. In this episode we discuss Juan's journey towards data science, how causality was close to his heart from the very beginning and why starting simple is a good thing. Juan shares how his background in physics and advanced geometry helps him tackle causal problems he faces daily in his work in the fields of marketing and pricing. "It's fundamental for decision-making" - he says when asked about the future of causal modeling and causal AI. We discuss the consequences of ignoring the causal structure in marketing problems. Finally, Juan shares how inaccurate world models contributed to a distaste for wearing gloves by someone dear to him. Ready to dive in? About The Guest Juan Orduz, Phd is a Senior Data Scientist at Wolt. He is a blogger and an open source contributor. Juan holds a PhD in geometric analysis. Connect with Juan: - Juan on LinkedIn - Juan on Twitter/X - Juan's Blog  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) Causal Bandits Team Project Coordinator: Taiba Malik Video Editors: Navneet S., Aleksander Molak Causal 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

Recorded on Sep 04, 2023 in London, United Kingdom. from causality import solutionA Python package that would allow us to address an arbitrary causal problem with a one-liner does not yet exist.Fortunately, there are other ways to implement and deploy causal solutions at scale. In this episode, Andrew shares his journey into causality and gives us a glimpse into the behind-the-scenes of his everyday work at causaLens. We discuss new ideas that Andrew and his team use to enhance the capabilities of available open-source causal packages, how they strive to build and maintain a highly modularized and open platform. Finally, we talk about the importance of team work and what Andrew's parents did to make him feel nurtured & supported. Ready? About The GuestAndrew Lawrence is the Director of Research at causaLens (https://causalens.com/) Connect with Andrew: Andrew on LinkedIn: https://www.linkedin.com/in/andrew-r-lawrence/ About The HostAleksander (Alex) Molak is an independent ML researcher, educator, entrepreneur and a best-selling author in the area of causality.Connect with Alex: Alex on the Internet: https://bit.ly/aleksander-molakLinksCode and BlogsDARA open-source framework (https://bit.ly/3Ql1VhF) causaLens GitHub (https://bit.ly/3QmoUJz) causaLens Blog (https://bit.ly/46TieJF) VideosBrady Neal Introduction to CausalityBooksBishop (2006) - Pattern Recognition and Machine Learning Molak (2023) - Causal Inference and Discovery in PythonPearl & Mackenzie (2019) - The Book of Why Peters et al (2017) - Elements of Causal Inference PapersFull list of papers see hereCausal 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

 Recorded on Aug 24, 2023 in Berlin, Germany. Does Causality Align with Bayesian Modeling? Structural causal models share a conceptual similarity with the models used in probabilistic programming. However, there are important theoretical differences between the two. Can we bridge them in practice? In this episode, we explore Thomas' journey into causality and discuss how his experience in Bayesian modeling accelerated his understanding of basic causal concepts. We delve into new causally-oriented developments in PyMC - an open-source Python probabilistic programming framework co-authored by Thomas - and discuss practical aspects of causal modeling drawing from Thomas' experience. "It's great to be wrong, and this is how we learn" - says Thomas, emphasizing the gradual and iterative nature of his and his team's successful projects. Further down the road, we take a look at the opportunities and challenges in uncertainty quantification, briefly discussing probabilistic programming, Bayesian deep learning and conformal prediction perspectives. Lastly, Thomas shares his personal journey from studying computer science, bioinformatics, and neuroscience, to becoming a major open-source contributor and an independent entrepreneur.Ready to dive in?About The GuestThomas Wiecki, Phd is a co-author of PyMC - one of the most recognizable Python probabilistic programming frameworks - and the CEO of PyMC Labs. Connect with Thomas: Thomas Wiecki on LinkedInThomas Wiecki on Twitter/XAbout 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 Internet: https://bit.ly/aleksander-molak Links PyMC's do-operator [Blog]  PyMC webapgeCausal 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

Recorded on Aug 14, 2023 in Frankfurt, Germany. Are Large Language Models (LLMs) causal? Some researchers have shown that advanced models like GPT-4 can perform very well on certain causal benchmarks. At the same time, from the theoretical point of view it's highly unlikely that these models can learn causal structures. Is it possible that large language models are not causal, but talk causality? In our conversation we explore this question from the point of view of the formalism proposed by Matej and his colleagues in their "Causal Parrots" paper. We also discuss Matej's journey from the dream of becoming a hacker to a successful AI and then causality researcher. Ready to dive in?Links EventsCausality Discussion Group (https://discuss.causality.link/) Eastern European Machine Learning Summer School (https://www.eeml.eu/home) Videos Prof. Moritz Helmstaedter on connectomicsBooks Molak (2023) - Causal Inference & Discovery in Python Pearl & Mackenzie (2019) - The Book of WhyPapers For full list of papers see the episode's description here.Causal 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