Causal Bandits Podcast

Send us a text [https://www.buzzsprout.com/twilio/text_messages/2272512/open_sms] *Causal Inference From Human Behavior, Reproducibility Crisis & The Power of Causal Graphs* Is Jonathan Heidt right that social media causes the mental health crisis in young people? If so, how can we be sure? Can other disciplines learn something from the reproducibility crisis in Psychology, and what is multiverse analysis? Join us for a conversation on causal inference from human behavior, the reproducibility crisis in sciences, and the power of causal graphs! ------------------------------------------------------------------------------------------------------ Audio version available on YouTube: https://youtu.be/YQetmI-y5gM Recorded on May 16, 2025, in Leipzig, Germany. ------------------------------------------------------------------------------------------------------ *About The Guest* Julia Rohrer, PhD, is a researcher and personality psychologist at the University of Leipzig. She's interested in the effects of birth order, age patterns in personality, human well-being, and causal inference. Her works have been published in top journals, including Nature Human Behavior. She has been an active advocate for increased research transparency, and she continues this mission as a senior editor of Psychological Science. Julia frequently gives talks about good practices in science and causal inference. You can read Julia's blog at https://www.the100.ci/ *Links* Papers - Rohrer, J. (2024) "Causal inference for psychologists who think that causal inference is not for them" (https://compass.onlinelibrary.wiley.com/doi/10.1111/spc3.12948) - Bailey, D., ..., Rohrer, J. et al (2024) "Causal inference on human behaviour" (https://www.nature.com/articles/s41562-024-01939-z.epdf) - Rohrer, J. et al (2024) "The Effects of Satisfaction with Different Domains of Life on Gen Inspiring Tech Leaders - The Technology Podcast [https://www.priceroberts.com/] Interviews with Tech Leaders and insights on the latest emerging technology trends. Listen on: Apple Podcasts [https://podcasts.apple.com/podcast/id1563026924]   Spotify [https://open.spotify.com/show/5ihWZxxIa2rEnzbx5tdKfH] Support the show [https://bit.ly/causalbanditspodcast] Causal Bandits Podcast Causal AI || Causal Machine Learning || Causal Inference & Discovery Web: https://causalbanditspodcast.com [https://causalbanditspodcast.com] Connect on LinkedIn: https://www.linkedin.com/in/aleksandermolak/ [https://www.youtube.com/redirect?event=video_description&redir_token=QUFFLUhqbnVza1RxV0dGb0xUVG9DUGY1SWJwem1IOTRqQXxBQ3Jtc0tsdXZPQmFqcmxiVXBNSDBNWjQ2N3ozM2tjd1pMVjg2MzVxTXl0dUp2cWxIUkFlN1ZhOXVWdFNlZmJvVFo5WmE3dEI2M0hxNVRkSmdWb095Y0ZRYnZaMFl5YmRuUEJRVlI4aEdtQk0zS3NGTFNRTnUzbw&q=https%3A%2F%2Fwww.linkedin.com%2Fin%2Faleksandermolak%2F&v=rM25vt_ZmFc] Join Causal Python Weekly: https://causalpython.io [https://www.youtube.com/redirect?event=video_description&redir_token=QUFFLUhqbXFINXB0cHYwZVZoMUFRVWMweUdObmVXVEJ4UXxBQ3Jtc0tuOEw3NmgxSEZES2lzelBycVp2b0JlR01TcnlZMVMwQ0F6SEFPOTZHeGlIQTZSd0pYdUF4a2xDdWJWODlTRHNzX0Y3bU1SNGVGWEY2UlA3S0Robm41OVNpVWdxTUhiLVVWQTQ1bnYyTDhkb1ZLOW5oQQ&q=https%3A%2F%2Fcausalpython.io%2F&v=rM25vt_ZmFc] The Causal Book: https://amzn.to/3QhsRz4 [https://www.youtube.com/redirect?event=video_description&redir_token=QUFFLUhqa05YQUd0LTN2MTV0eFN5ek1LUjVjM3JDX0dEUXxBQ3Jtc0trS3F0YWlwUmtDOGZmOHJYV3VLdEwwaWx1U0xxUVJ3aTF4TGF2ODAwMENBc2dnRks1V3ozUmp3ZnluN2VCWWxFSmJmRlZHZjUwYkdtNFI2OFhua2FpMm9LcjFxVUhrRTBJakM1NjZPT3BlblN1Z3Y3aw&q=https%3A%2F%2Famzn.to%2F3QhsRz4&v=rM25vt_ZmFc]

Send us a text [https://www.buzzsprout.com/twilio/text_messages/2272512/open_sms] *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/8yWKQqNFrmY [https://youtu.be/8yWKQqNFrmY] Recorded 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/ [https://www.linkedin.com/in/amitshar/] - Amit on BlueSky: - Amit 's web page: http://amitsharma.in/ [http://amitsharma.in/] *About The Host* Aleksande Inspiring Tech Leaders - The Technology Podcast [https://www.priceroberts.com/] Interviews with Tech Leaders and insights on the latest emerging technology trends. Listen on: Apple Podcasts [https://podcasts.apple.com/podcast/id1563026924]   Spotify [https://open.spotify.com/show/5ihWZxxIa2rEnzbx5tdKfH] Support the show [https://bit.ly/causalbanditspodcast] Causal Bandits Podcast Causal AI || Causal Machine Learning || Causal Inference & Discovery Web: https://causalbanditspodcast.com [https://causalbanditspodcast.com] Connect on LinkedIn: https://www.linkedin.com/in/aleksandermolak/ [https://www.youtube.com/redirect?event=video_description&redir_token=QUFFLUhqbnVza1RxV0dGb0xUVG9DUGY1SWJwem1IOTRqQXxBQ3Jtc0tsdXZPQmFqcmxiVXBNSDBNWjQ2N3ozM2tjd1pMVjg2MzVxTXl0dUp2cWxIUkFlN1ZhOXVWdFNlZmJvVFo5WmE3dEI2M0hxNVRkSmdWb095Y0ZRYnZaMFl5YmRuUEJRVlI4aEdtQk0zS3NGTFNRTnUzbw&q=https%3A%2F%2Fwww.linkedin.com%2Fin%2Faleksandermolak%2F&v=rM25vt_ZmFc] Join Causal Python Weekly: https://causalpython.io [https://www.youtube.com/redirect?event=video_description&redir_token=QUFFLUhqbXFINXB0cHYwZVZoMUFRVWMweUdObmVXVEJ4UXxBQ3Jtc0tuOEw3NmgxSEZES2lzelBycVp2b0JlR01TcnlZMVMwQ0F6SEFPOTZHeGlIQTZSd0pYdUF4a2xDdWJWODlTRHNzX0Y3bU1SNGVGWEY2UlA3S0Robm41OVNpVWdxTUhiLVVWQTQ1bnYyTDhkb1ZLOW5oQQ&q=https%3A%2F%2Fcausalpython.io%2F&v=rM25vt_ZmFc] The Causal Book: https://amzn.to/3QhsRz4 [https://www.youtube.com/redirect?event=video_description&redir_token=QUFFLUhqa05YQUd0LTN2MTV0eFN5ek1LUjVjM3JDX0dEUXxBQ3Jtc0trS3F0YWlwUmtDOGZmOHJYV3VLdEwwaWx1U0xxUVJ3aTF4TGF2ODAwMENBc2dnRks1V3ozUmp3ZnluN2VCWWxFSmJmRlZHZjUwYkdtNFI2OFhua2FpMm9LcjFxVUhrRTBJakM1NjZPT3BlblN1Z3Y3aw&q=https%3A%2F%2Famzn.to%2F3QhsRz4&v=rM25vt_ZmFc]

Send us a text [https://www.buzzsprout.com/twilio/text_messages/2272512/open_sms]  📽️ FREE Online Course on Causality [https://causalsecrets.com/]  📕 Causal Inference & Discovery in Python [https://amzn.to/3SKRXIw] Causal Secrets of N=1 Experiments Join me for a one of a kind conversation on the opportunities and challenges of n-of-1 trials, Eric's causal journey, his path into statistics, his love of sci-fi, and how single-subject experiments could reshape personalized medicine. Video version available here [https://youtu.be/gN-6DfhCguA] About The Guest Dr. Eric J. Daza is a biostatistician and health data scientist with over 22 years of experience (Cornell, UNC Chapel Hill, Stanford). He works at Boehringer Ingelheim. Eric is a creator of Stats-of-1, a health innovation newsletter & podcast on n-of-1 trials, single-case designs, switchback experiments, and personal AI for digital health/medicine. All views and opinions expressed by Dr. Eric J. Daza represent no one but himself. These views and opinions do not represent the views and opinions of his employer. Connect with Eric: * Eric on LinkedIn [https://www.linkedin.com/in/ericjdaza/] * Eric on BlueSky [https://bsky.app/profile/ericjdaza.com] * Eric's web page [https://ericjdaza.com/] About The Host Connect with Alex: * Alex on the Internet [https://bit.ly/aleksander-molak]  *  👉🏼 Consulting and Causal AI Training For Your Team: hello causalpython.io Episode Links Papers * Daza (2018) - "Causal Analysis of Self-tracked Time Series Data Using a Counterfactual Framework for N-of-1 Trials [https://www.thieme-connect.com/products/ejournals/abstract/10.3414/ME16-02-0044]" * Matias, Daza et al (2022) - "https://journals.sagepub.com/doi/10.1177/20552076221120725 Inspiring Tech Leaders - The Technology Podcast [https://www.priceroberts.com/] Interviews with Tech Leaders and insights on the latest emerging technology trends. Listen on: Apple Podcasts [https://podcasts.apple.com/podcast/id1563026924]   Spotify [https://open.spotify.com/show/5ihWZxxIa2rEnzbx5tdKfH] Support the show [https://bit.ly/causalbanditspodcast] Causal Bandits Podcast Causal AI || Causal Machine Learning || Causal Inference & Discovery Web: https://causalbanditspodcast.com [https://causalbanditspodcast.com] Connect on LinkedIn: https://www.linkedin.com/in/aleksandermolak/ [https://www.youtube.com/redirect?event=video_description&redir_token=QUFFLUhqbnVza1RxV0dGb0xUVG9DUGY1SWJwem1IOTRqQXxBQ3Jtc0tsdXZPQmFqcmxiVXBNSDBNWjQ2N3ozM2tjd1pMVjg2MzVxTXl0dUp2cWxIUkFlN1ZhOXVWdFNlZmJvVFo5WmE3dEI2M0hxNVRkSmdWb095Y0ZRYnZaMFl5YmRuUEJRVlI4aEdtQk0zS3NGTFNRTnUzbw&q=https%3A%2F%2Fwww.linkedin.com%2Fin%2Faleksandermolak%2F&v=rM25vt_ZmFc] Join Causal Python Weekly: https://causalpython.io [https://www.youtube.com/redirect?event=video_description&redir_token=QUFFLUhqbXFINXB0cHYwZVZoMUFRVWMweUdObmVXVEJ4UXxBQ3Jtc0tuOEw3NmgxSEZES2lzelBycVp2b0JlR01TcnlZMVMwQ0F6SEFPOTZHeGlIQTZSd0pYdUF4a2xDdWJWODlTRHNzX0Y3bU1SNGVGWEY2UlA3S0Robm41OVNpVWdxTUhiLVVWQTQ1bnYyTDhkb1ZLOW5oQQ&q=https%3A%2F%2Fcausalpython.io%2F&v=rM25vt_ZmFc] The Causal Book: https://amzn.to/3QhsRz4 [https://www.youtube.com/redirect?event=video_description&redir_token=QUFFLUhqa05YQUd0LTN2MTV0eFN5ek1LUjVjM3JDX0dEUXxBQ3Jtc0trS3F0YWlwUmtDOGZmOHJYV3VLdEwwaWx1U0xxUVJ3aTF4TGF2ODAwMENBc2dnRks1V3ozUmp3ZnluN2VCWWxFSmJmRlZHZjUwYkdtNFI2OFhua2FpMm9LcjFxVUhrRTBJakM1NjZPT3BlblN1Z3Y3aw&q=https%3A%2F%2Famzn.to%2F3QhsRz4&v=rM25vt_ZmFc]

Send us a text [https://www.buzzsprout.com/twilio/text_messages/2272512/open_sms] From Quantum Causal Models to Causal AI at Spotify Ciarán 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 YouTube [https://youtu.be/CzL0pV6LyRk] Recorded on Nov 6, 2024 in Dublin, Ireland. ------------------------------------------------------------------------------------------------------ About The Guest Ciarán 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 field Inspiring Tech Leaders - The Technology Podcast [https://www.priceroberts.com/] Interviews with Tech Leaders and insights on the latest emerging technology trends. Listen on: Apple Podcasts [https://podcasts.apple.com/podcast/id1563026924]   Spotify [https://open.spotify.com/show/5ihWZxxIa2rEnzbx5tdKfH] Support the show [https://bit.ly/causalbanditspodcast] Causal Bandits Podcast Causal AI || Causal Machine Learning || Causal Inference & Discovery Web: https://causalbanditspodcast.com [https://causalbanditspodcast.com] Connect on LinkedIn: https://www.linkedin.com/in/aleksandermolak/ [https://www.youtube.com/redirect?event=video_description&redir_token=QUFFLUhqbnVza1RxV0dGb0xUVG9DUGY1SWJwem1IOTRqQXxBQ3Jtc0tsdXZPQmFqcmxiVXBNSDBNWjQ2N3ozM2tjd1pMVjg2MzVxTXl0dUp2cWxIUkFlN1ZhOXVWdFNlZmJvVFo5WmE3dEI2M0hxNVRkSmdWb095Y0ZRYnZaMFl5YmRuUEJRVlI4aEdtQk0zS3NGTFNRTnUzbw&q=https%3A%2F%2Fwww.linkedin.com%2Fin%2Faleksandermolak%2F&v=rM25vt_ZmFc] Join Causal Python Weekly: https://causalpython.io [https://www.youtube.com/redirect?event=video_description&redir_token=QUFFLUhqbXFINXB0cHYwZVZoMUFRVWMweUdObmVXVEJ4UXxBQ3Jtc0tuOEw3NmgxSEZES2lzelBycVp2b0JlR01TcnlZMVMwQ0F6SEFPOTZHeGlIQTZSd0pYdUF4a2xDdWJWODlTRHNzX0Y3bU1SNGVGWEY2UlA3S0Robm41OVNpVWdxTUhiLVVWQTQ1bnYyTDhkb1ZLOW5oQQ&q=https%3A%2F%2Fcausalpython.io%2F&v=rM25vt_ZmFc] The Causal Book: https://amzn.to/3QhsRz4 [https://www.youtube.com/redirect?event=video_description&redir_token=QUFFLUhqa05YQUd0LTN2MTV0eFN5ek1LUjVjM3JDX0dEUXxBQ3Jtc0trS3F0YWlwUmtDOGZmOHJYV3VLdEwwaWx1U0xxUVJ3aTF4TGF2ODAwMENBc2dnRks1V3ozUmp3ZnluN2VCWWxFSmJmRlZHZjUwYkdtNFI2OFhua2FpMm9LcjFxVUhrRTBJakM1NjZPT3BlblN1Z3Y3aw&q=https%3A%2F%2Famzn.to%2F3QhsRz4&v=rM25vt_ZmFc]

Send us a text [https://www.buzzsprout.com/twilio/text_messages/2272512/open_sms] Stefan 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 [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 [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 [https://arxiv.org/abs/2407.03094]) >> WWW: https://www.som.lmu.de/ai/ [https://www.som.lmu.de/ai/] >> LinkedIn: https://www.linkedin.com/in/stefan-feuerriegel/ [https://www.linkedin.com/in/stefan-feuerriegel/] Inspiring Tech Leaders - The Technology Podcast [https://www.priceroberts.com/] Interviews with Tech Leaders and insights on the latest emerging technology trends. Listen on: Apple Podcasts [https://podcasts.apple.com/podcast/id1563026924]   Spotify [https://open.spotify.com/show/5ihWZxxIa2rEnzbx5tdKfH] Support the show [https://bit.ly/causalbanditspodcast] Causal Bandits Podcast Causal AI || Causal Machine Learning || Causal Inference & Discovery Web: https://causalbanditspodcast.com [https://causalbanditspodcast.com] Connect on LinkedIn: https://www.linkedin.com/in/aleksandermolak/ [https://www.youtube.com/redirect?event=video_description&redir_token=QUFFLUhqbnVza1RxV0dGb0xUVG9DUGY1SWJwem1IOTRqQXxBQ3Jtc0tsdXZPQmFqcmxiVXBNSDBNWjQ2N3ozM2tjd1pMVjg2MzVxTXl0dUp2cWxIUkFlN1ZhOXVWdFNlZmJvVFo5WmE3dEI2M0hxNVRkSmdWb095Y0ZRYnZaMFl5YmRuUEJRVlI4aEdtQk0zS3NGTFNRTnUzbw&q=https%3A%2F%2Fwww.linkedin.com%2Fin%2Faleksandermolak%2F&v=rM25vt_ZmFc] Join Causal Python Weekly: https://causalpython.io [https://www.youtube.com/redirect?event=video_description&redir_token=QUFFLUhqbXFINXB0cHYwZVZoMUFRVWMweUdObmVXVEJ4UXxBQ3Jtc0tuOEw3NmgxSEZES2lzelBycVp2b0JlR01TcnlZMVMwQ0F6SEFPOTZHeGlIQTZSd0pYdUF4a2xDdWJWODlTRHNzX0Y3bU1SNGVGWEY2UlA3S0Robm41OVNpVWdxTUhiLVVWQTQ1bnYyTDhkb1ZLOW5oQQ&q=https%3A%2F%2Fcausalpython.io%2F&v=rM25vt_ZmFc] The Causal Book: https://amzn.to/3QhsRz4 [https://www.youtube.com/redirect?event=video_description&redir_token=QUFFLUhqa05YQUd0LTN2MTV0eFN5ek1LUjVjM3JDX0dEUXxBQ3Jtc0trS3F0YWlwUmtDOGZmOHJYV3VLdEwwaWx1U0xxUVJ3aTF4TGF2ODAwMENBc2dnRks1V3ozUmp3ZnluN2VCWWxFSmJmRlZHZjUwYkdtNFI2OFhua2FpMm9LcjFxVUhrRTBJakM1NjZPT3BlblN1Z3Y3aw&q=https%3A%2F%2Famzn.to%2F3QhsRz4&v=rM25vt_ZmFc]