Send us a text 📽️ FREE Online Course on Causality 📕 Causal Inference & Discovery in PythonCausal Secrets of N=1 ExperimentsJoin 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 hereAbout The GuestDr. ​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 LinkedInEric on BlueSkyEric's web pageAbout The HostConnect with Alex:Alex on the Internet  👉🏼 Consulting and Causal AI Training For Your Team: hello <at> causalpython.ioEpisode LinksPapersDaza (2018) - "Causal Analysis of Self-tracked Time Series Data Using a Counterfactual Framework for N-of-1 Trials"Matias, Daza et al (2022) - "What possibly affects nighttime heart rate? Conclusions from N-of-1 observational data"BooksAsimov, I (1991) - "Foundation"AppsStudyUWebpagesStats-of-1Support 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

Ciarán Gilligan-Lee, Head of the Causal Inference Research Lab at Spotify and an Honorary Associate Professor at UCL, delves into fascinating intersections of quantum physics and causal AI. He discusses how understanding causality can enhance business outcomes at Spotify while unraveling the complexities of causal inference versus correlation. Ciarán also shares insights from his innovative work combining causal methods with astrophysics, exploring galaxy evolution and environmental factors in star formation, all while reflecting on influential literature that shaped his journey.

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)=============================🔔Unlock the power of Python in AI and machine learning. Subscribe for simple insights into Causal Inference and Discovery.   / @causalpython   ✅ Important Links to Follow🔗Medium Blog  / aleksander-molak   🔗Newsletter Webhttps://causalpython.io/ 🔗 Linkshttps://bit.ly/m/alex-bio 🔗 GitHubhttps://github.com/AlxndrMlk ✅  Stay Connected With Me.👉Twitter (X):   / aleksandermolak    👉Linkedin:   / aleksandermolak   👉Facebook:   / causalpython    👉Instagram:   / alex.molak   👉Tiktok:   / alex.molak   👉Causal Bandits Podcast Website: https://causalbanditspodcast.com/✅ For Business Inquiries:  hello@causalpython.io =============================✅  Recommended Playlists👉 Causal Bandits Podcast   • Matej Zečević On Causality In AI: Can...   👉 Causal Bandits Podcast Shorts   • Answer with Causal Identification #po..=================================© Causal Python with Alex MolakSupport the showCausal Bandits PodcastCausal AI || Causal Machine Learning || Causal Inference & DiscoveryWeb: https://causalbanditspodcast.comConnect on LinkedIn: https://www.linkedin.com/in/aleksandermolak/Join Causal Python Weekly: https://causalpython.io The Causal Book: https://amzn.to/3QhsRz4

Send us a textWhich models work best for causal discovery and double machine learning?In this extra episode, we present 4 more conversations with the researchers presenting their work at the CLeaR 2024 conference in Los Angeles, California.What you'll learn:- Which causal discovery models perform best with their default hyperparameters?- How to tune your double machine learning model?- Does putting your paper on ArXiv early increase its chances of being accepted at a conference?- How to deal with causal representation learning with multiple latent interventions?Time codes:00:24 Damian Machlanski - Hyperparameter Tuning for Causal Discovery08:52 Oliver Schacht - Hyperparameter Tuning for DML14:41 Yanai Elazar - Causal Effect of Early ArXiving on Paper Acceptance18:53 Simon Bing - Identifying Linearly-Mixed Causal Representations from Multi-Node Interventions=============================🔔Unlock the power of Python in AI and machine learning. Subscribe for simple insights into Causal Inference and Discovery.https://www.youtube.com/@CausalPython/?sub_confirmation=1 ✅  Stay Connected With Me.👉Twitter (X): https://twitter.com/AleksanderMolak  👉Linkedin: https://www.linkedin.com/in/aleksandermolak/ 👉Facebook: https://www.facebook.com/CausalPython  👉Instagram: https://www.instagram.com/alex.molak/ 👉Tiktok: https://www.tiktok.com/@alex.molak 👉Causal Bandits Podcast Website: https://causalbanditspodcast.com/✅ For Business Inquiries:  hello@causalpython.io =============================✅  About Causal Python with Alex Molak.Welcome to my official YouTube channel, Causal Python, with Alex Molak. Dive into the fascinating world of Causal AI, unraveling the complexities of Causal Inference and Discovery with Python. My content simplifies these intricate topics, making them accessible whether you’re starting or advancing your knowledge. Here, I explore the intersections of causality, AI, machine learning, optimization, and decision-making, all through Python’s versatile capabilities.This is also the home of The Causal Bandits Podcast. For more insightful discussions, check out my Causal Bandit Podcast Website.============Support the showCausal Bandits PodcastCausal AI || Causal Machine Learning || Causal Inference & DiscoveryWeb: https://causalbanditspodcast.comConnect on LinkedIn: https://www.linkedin.com/in/aleksandermolak/Join Causal Python Weekly: https://causalpython.io The Causal Book: https://amzn.to/3QhsRz4

Send us a textRoot cause analysis, model explanations, causal discovery.Are we facing a missing benchmark problem?Or not anymore?In this special episode, we travel to Los Angeles to talk with researchers at the forefront of causal research, exploring their projects, key insights, and the challenges they face in their work.Time codes:0:15 - 02:40    Kevin Debeire2:41 - 06:37    Yuchen Zhu06:37 - 10:09   Konstantin Göbler10:09 - 17:05   Urja Pawar17:05 - 23:16  William OrchardEnjoy!Support the showCausal Bandits PodcastCausal AI || Causal Machine Learning || Causal Inference & DiscoveryWeb: https://causalbanditspodcast.comConnect on LinkedIn: https://www.linkedin.com/in/aleksandermolak/Join Causal Python Weekly: https://causalpython.io The Causal Book: https://amzn.to/3QhsRz4

Send us a text *Causal Bandits at AAAI 2024 || Part 2*In this special episode we interview researchers who presented their work at AAAI 2024 in Vancouver, Canada.Time codes: 00:12 - 04:18 Kevin Xia (Columbia University) - Transportability4:19 - 9:53 Patrick Altmeyer (Delft) - Explainability & black-box models9:54 - 12:24 Lokesh Nagalapatti (IIT Bombay) - Continuous treatment effects12:24 - 16:06 Golnoosh Farnadi (McGill University) - Causality & responsible AI16:06 - 17:37 Markus Bläser (Saarland University) - Fast identification of causal parameters17:37 - 22:37 Devendra Singh Dhami (TU/e) - The future of causal AI Support the showCausal Bandits PodcastCausal AI || Causal Machine Learning || Causal Inference & DiscoveryWeb: https://causalbanditspodcast.comConnect on LinkedIn: https://www.linkedin.com/in/aleksandermolak/Join Causal Python Weekly: https://causalpython.io The Causal Book: https://amzn.to/3QhsRz4

Send us a text Causal Bandits at AAAI 2024 || Part 1In this special episode we interview researchers who presented their work at AAAI 2024 in Vancouver, Canada and participants of our workshop on causality and large language models (LLMs)Time codes:00:00 Intro00:20 Osman Ali Mian (CISPA) - Adaptive causal discovery for time series04:35 Emily McMilin (Independent/Meta) - LLMs, causality & selection bias07:36 Scott Mueller (UCLA) - Causality for EV incentives12:41 Andrew Lampinen (Google DeepMind) - Causality from passive data15:16 Ali Edalati (Huawei) - About Causal Parrots workshop15:26 Adbelrahman Zayed (MILA) - About Causal Parrots workshop Support the showCausal Bandits PodcastCausal AI || Causal Machine Learning || Causal Inference & DiscoveryWeb: https://causalbanditspodcast.comConnect on LinkedIn: https://www.linkedin.com/in/aleksandermolak/Join Causal Python Weekly: https://causalpython.io The Causal Book: https://amzn.to/3QhsRz4

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

Send us a textCan we say something about YOUR personal treatment effect?The estimation of individual treatment effects is the Holy Grail of personalized medicine.It's also extremely difficult.Yet, Scott is not discouraged from studying this topic.In fact, he quit a pretty successful business to study it.In a series of papers, Scott describes how combining experimental and observational data can help us understand individual causal effects.Although this sounds enigmatic to many, the intuition behind this mechanism is simpler than you might think.In the episode we discuss:🔹 What made Scott quit a successful business he founded and study causal inference?🔹 How a false conviction about his own skills helped him learn? 🔹 What are individual treatment effects?🔹 Can we really say something about individual treatment effects?Ready to dive in?About The GuestScott Mueller is a researcher and a PhD candidate in causal modeling at UCLA, supervised by Prof. Judea Pearl. He's a serial entrepreneur and the founder of UCode, a coding school for kids. His current research focuses on the estimation of individual treatment effects and their bounds. He works under the supervision of professor Judea Pearl.Connect with Scott:- Scott on Twitter/X - Scott's webpageAbout The HostAleksander (Alex) Molak is an independent machine learning researcher, educator, entrepreneur and a best-selling author in the area of causality.Connect with Alex:- Alex on the InternetSupport the showCausal Bandits PodcastCausal AI || Causal Machine Learning || Causal Inference & DiscoveryWeb: https://causalbanditspodcast.comConnect on LinkedIn: https://www.linkedin.com/in/aleksandermolak/Join Causal Python Weekly: https://causalpython.io The Causal Book: https://amzn.to/3QhsRz4