Causal Bandits Podcast

Send us a textSupport the showVideo version available on YouTubeRecorded on Sep 27, 2023 in München, GermanyFrom 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 Full list of papers hereBooks- Molak (2023) - 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

Send us a textSupport the showVideo version of this episode is available on YouTubeRecorded on Oct 15, 2023 in São Paulo, BrazilCausal Inference in Fintech? For Brave and True OnlyFrom 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) –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

Send us a textSupport the showVideo version available on YouTube Recorded on Sep 13, 2023 in Beit El'Azari, Israel The 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 - 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

Send us a textSupport the showVideo version available on YouTubeRecorded on Sep 4, 2023 in London, UKA 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 makingSupport 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 textSupport the showVideo version of this episode is available hereRecorded on Sep 5, 2023 in Oxford, UKHave 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 LEveryday 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

Alicia Curth, a machine learning researcher specializing in causal machine learning, discusses topics such as the double descent phenomenon, conditional average treatment effect estimators, challenges in working with Kate models, curiosity-driven studies, sensitivity analysis in causal research, and contrasting approaches in machine learning and statistics/econometrics.

In this engaging discussion, Naftali Weinberger, a researcher at Munich Center for Mathematical Philosophy, explores the intricate concepts of causality and dynamical systems. He unpacks the significance of time scales in causal analysis and critiques Bertrand Russell's view on causation's relevance. The conversation dives into how modern physics is reshaping our understanding of causality, alongside the challenges posed by big data. Naftali also shares valuable advice for those beginning their journey in causal inference, promising to challenge your perception of reality!

Daniel Ebenhöch, a lead engineer with a fascinating background in child experimentation, discusses the pivotal role of causality in autonomous driving. He shares insights on the challenges of developing causal models and counterfactual reasoning. A key focus is on optimizing decision-making to enhance safety while navigating corner cases. Ebenhöch highlights the importance of collaboration between diverse scientific disciplines and provides advice for newcomers to the field, emphasizing curiosity and adaptability as essential traits.

Send us a textSupport the show Video version available on YouTubeRecorded on Aug 25, 2023 in Berlin, Germany 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-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

Send us a textSupport the showVideo version of this episode is available on YouTubeRecorded on Aug 24, 2023 in Berlin, GermanyDoes 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 onEveryday 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