Podcasts about causal

Today's clip is from episode 137 of the podcast, with Robert Ness.Alex and Robert discuss the intersection of causal inference and deep learning, emphasizing the importance of understanding causal concepts in statistical modeling. The discussion also covers the evolution of probabilistic machine learning, the role of inductive biases, and the potential of large language models in causal analysis, highlighting their ability to translate natural language into formal causal queries.Get the full conversation here.Attend Alex's tutorial at PyData Berlin: A Beginner's Guide to State Space Modeling Intro to Bayes Course (first 2 lessons free)Advanced Regression Course (first 2 lessons free)Our theme music is « Good Bayesian », by Baba Brinkman (feat MC Lars and Mega Ran). Check out his awesome work!Visit our Patreon page to unlock exclusive Bayesian swag ;)TranscriptThis is an automatic transcript and may therefore contain errors. Please get in touch if you're willing to correct them.

If you’ve ever stood frozen in the kitchen, fully aware of what needs doing — and still couldn’t start — this episode is for you. This week’s Quick Reset tears apart the toxic myth that ADHD mums just need to “try harder.” Jane unpacks what’s actually happening when your brain stalls, and why shame, not laziness, is often the real culprit. From inner critics echoing old failures to the neuroscience behind executive dysfunction, this is a raw, validating, and darkly funny call to stop blaming effort — and start working with your brain instead. You’ll learn practical, low-pressure strategies to get started (no, not with a new app), and finally understand why “just do it” advice doesn’t just fail ADHD mums — it hurts us. ✨ IN THIS RESET: The damaging myth of laziness and willpower How shame shuts down executive function Why ADHD is not about motivation — it’s about regulation What to say to yourself instead of “I just need to focus” Brain-based strategies for task initiation that actually help Real talk about fridge purchases, hyperfocus, and starting vs finishing Why “trying harder” never fixed burnout — and never will

Steph and Molly are here this week for the Weekly Dish to dish out the deal with "deep causal hosting."See Privacy Policy at https://art19.com/privacy and California Privacy Notice at https://art19.com/privacy#do-not-sell-my-info.

Proudly sponsored by PyMC Labs, the Bayesian Consultancy. Book a call, or get in touch!Intro to Bayes Course (first 2 lessons free)Advanced Regression Course (first 2 lessons free)Our theme music is « Good Bayesian », by Baba Brinkman (feat MC Lars and Mega Ran). Check out his awesome work!Visit our Patreon page to unlock exclusive Bayesian swag ;)Takeaways:Causal assumptions are crucial for statistical modeling.Deep learning can be integrated with causal models.Statistical rigor is essential in evaluating LLMs.Causal representation learning is a growing field.Inductive biases in AI should match key mechanisms.Causal AI can improve decision-making processes.The future of AI lies in understanding causal relationships.Chapters:00:00 Introduction to Causal AI and Its Importance16:34 The Journey to Writing Causal AI28:05 Integrating Graphical Causality with Deep Learning40:10 The Evolution of Probabilistic Machine Learning44:34 Practical Applications of Causal AI with LLMs49:48 Exploring Multimodal Models and Causality56:15 Tools and Frameworks for Causal AI01:03:19 Statistical Rigor in Evaluating LLMs01:12:22 Causal Thinking in Real-World Deployments01:19:52 Trade-offs in Generative Causal Models01:25:14 Future of Causal Generative ModelingThank you to my Patrons for making this episode possible!Yusuke Saito, Avi Bryant, Ero Carrera, Giuliano Cruz, James Wade, Tradd Salvo, William Benton, James Ahloy, Robin Taylor,, Chad Scherrer, Zwelithini Tunyiswa, Bertrand Wilden, James Thompson, Stephen Oates, Gian Luca Di Tanna, Jack Wells, Matthew Maldonado, Ian Costley, Ally Salim, Larry Gill, Ian Moran, Paul Oreto, Colin Caprani, Colin Carroll, Nathaniel Burbank, Michael Osthege, Rémi Louf, Clive Edelsten, Henri Wallen, Hugo Botha, Vinh Nguyen, Marcin Elantkowski, Adam C. Smith, Will Kurt, Andrew Moskowitz, Hector Munoz, Marco Gorelli, Simon Kessell, Bradley Rode, Patrick Kelley, Rick Anderson, Casper de Bruin, Philippe Labonde, Michael Hankin, Cameron Smith, Tomáš Frýda, Ryan Wesslen, Andreas Netti, Riley King, Yoshiyuki Hamajima, Sven De Maeyer, Michael DeCrescenzo, Fergal M, Mason Yahr, Naoya Kanai, Aubrey Clayton, Jeannine Sue, Omri Har Shemesh, Scott Anthony Robson, Robert Yolken, Or Duek, Pavel Dusek, Paul Cox, Andreas Kröpelin, Raphaël R, Nicolas Rode, Gabriel Stechschulte, Arkady, Kurt TeKolste, Marcus Nölke, Maggi Mackintosh, Grant...

Discover how causal AI transforms marketing analytics by solving the correlation vs. causation dilemma. Learn why outdated Marketing Mix Modeling (MMM) can't keep up, and how causal AI provides actionable, real-time insights for CMOs and CFOs. SHOWPAGE:  https://www.ninjacat.io/blog/wgm-podcast-make-better-bets-with-causal-ai  © 2025, NinjaCat

Hayley B. Gershengorn, MD, joins CHEST® Journal Podcast Moderator, Gretchen Winter, MD, to discuss her research into the effect of double-blind peer review on manuscript acceptance for authors by gender and presumed English fluency at CHEST.  DOI: 10.1016/j.chest.2025.02.016 Disclaimer: The purpose of this activity is to expand the reach of CHEST content through awareness, critique, and discussion. All articles have undergone peer review for methodologic rigor and audience relevance. Any views asserted are those of the speakers and are not endorsed by CHEST. Listeners should be aware that speakers' opinions may vary and are advised to read the full corresponding journal article(s) for complete context. This content should not be used as a basis for medical advice or treatment, nor should it substitute the judgment used by clinicians in the practice of evidence-based medicine.  

What's up everyone, today we have the pleasure of sitting down with Rajeev Nair, Co-Founder and Chief Product Officer at Lifesight. Summary: Rajeev believes measurement only works when it's unified or multi-modal, a stack that blends multi-touch attribution, incrementality, media mix modeling and causal AI, each used for the decision it fits. At Lifesight, that means using causal machine learning to surface hidden experiments in messy historical data and designing geo tests that reveal what actually drives lift. Attribution alone can't tell you what changed outcomes. Rajeev's team moved past dashboards and built a system that focuses on clarity, not correlation. Attribution handles daily tweaks. MMM guides long-term planning. Experiments validate what's real. Each tool plays a role, but none can stand alone.About RajeevRajeev Nair is the Co-Founder and Chief Product Officer at Lifesight, where he's spent the last several years shaping how modern marketers measure impact. Before that, he led product at Moda and served as a business intelligence analyst at Ebizu. He began his career as a technical business analyst at Infosys, building a foundation in data and systems thinking that still drives his work today.Digital Astrology and the Attribution IllusionLifesight started by building traditional attribution tools focused on tracking user journeys and distributing credit across touchpoints using ID graphs. The goal was to help brands understand which interactions influenced conversions. But Rajeev and his team quickly realized that attribution alone didn't answer the core question their customers kept asking: what actually drove incremental revenue? In response, they shifted gears around 2019, moving toward incrementality testing. They began with exposed versus synthetic control groups, then evolved to more scalable, identity-agnostic methods like geo testing. This pivot marked a fundamental change in their product philosophy; from mapping behavior to measuring causal impact.Rajeeve shares his thoughts on multi-touch attribution and the evolution of the space.The Dilution of The Term AttributionAttribution has been hijacked by tracking. Rajeev points straight at the rot. What used to be a way to understand which actions actually led to a customer buying something has become little more than a digital breadcrumb trail. Marketers keep calling it attribution, but what they're really doing is surveillance. They're collecting events and assigning credit based on who touched what ad and when, even if none of it actually changed the buyer's mind.The biggest failure here is causality. Rajeev is clear about this. Attribution is supposed to tell you what caused an outcome. Not what appeared next to it. Not what someone happened to click on right before. Actual cause and effect. Instead, we get dashboards full of correlation dressed up as insight. You might see a spike in conversions and assume it was the retargeting campaign, but you're building castles on sand if you can't prove causality.Then comes the complexity problem. Today's marketing stack is a jungle. You have:Paid ads across five different platformsOrganic contentDiscountsSeasonal shiftsPricing changesProduct updatesAll these things impact results, but most attribution models treat them like isolated variables. They don't ask, “What moved the needle more than it would've moved otherwise?” They ask, “Who touched the user last before they bought?” That's not measurement. That's astrology for marketers.“Attribution, in today's marketing context, has just come to mean tracking. The word itself has been diluted.”Multi-touch attribution doesn't save you either. It distributes credit differently, but it's still built on flawed data and weak assumptions. If you're measuring everything and understanding nothing, you're just spending more money to stay confused. Real marketing optimization requires incrementality analysis, not just a prettier funnel chart.To Measure What Caused a Sale, You Need ExperimentsEven with perfect data, attribution keeps lying. Rajeev learned that the hard way. His team chased the attribution grail by building identity graphs so detailed they could probably tell you what toothpaste a customer used. They stitched together first-party and third-party data, mapped the full user journey, and connected every touchpoint from TikTok to in-store checkout. Then they ran the numbers. What came back wasn't insight. It was statistical noise.Every marketing team that has sunk months into journey mapping has hit the same wall. At the bottom of the funnel, conversion paths light up like a Christmas tree. Retargeting ads, last-clicked emails, discount codes, they all scream high correlation with purchase. The logic feels airtight until you realize it's just recency bias with a data export. These touchpoints show up because they're close to conversion. That doesn't mean they caused it.“Causality is essentially correlation plus bias. Can we somehow manage the bias so that we could interpret the observed correlation as causality?”What Rajeev means is that while correlation on its own proves nothing, it's still the starting point. You need correlation to even guess at a causal link, but then you have to strip out all the bias (timing, selection, confounding variables) before you can claim anything actually drove the outcome. It's a messy process, and attribution data alone doesn't get you there.That's the puzzle. You can't infer real marketing effectiveness just from journey data. You can't say the billboard drove walk-ins if everyone had to walk past it to enter the store. You can't say coupons created conversions if they were handed out after someone had already walked in. Attribution doesn't answer those questions. It only tells you what happened. It doesn't explain why it happened.To measure causality, you need experiments. Rajeev gives it straight: run controlled tests. Put a billboard at one store, skip it at another. Offer discounts to some, hold them back from others. Then compare outcomes. Only when you hold a variable constant and see lift can you say something worked. Attribution on its own is just a correlation engine. And correlation, without real-world intervention, tells you absolutely nothing useful.Key takeaway: Attribution data without controlled testing isn't useful. If you want to know what drives results, design experiments. Stop treating customer journeys like gospel. Use journey data as a starting point, then isolate variables and measure actual lift. That way you can make real decisions instead of retroactively rationalizing whatever got funded last quarter.The Limitations of Incrementality Tests and How Quasi-Experiments Can HelpMost teams think they're being scientific when they run an incrementality test. But the truth is, these tests are fragile. Geo tests are high-effort and easy to mess up. Quasi experiments are directional at best and misleading at worst. If you're not careful with design, timing, and interpretation, you'll end up with results that look rigorous… but aren't.Why Most Teams Get Geo Testing Completely WrongGeo testing gets romanticized as this high-integrity measurement method, but most teams treat it like a side quest. They run it once, complain it was expensive, then go back to attribution dashboards because they're easier to screenshot in a slide deck. The truth is, geo testing takes guts. It means pulling spend from regions that bring in real revenue. That's not a simulation. It's a real-world test with real-world consequences.Rajeev breaks it down with...

Nathalie BajosSanté publique (2024-2025)Collège de FranceAnnée 2024-2025Colloque - La production sociale des inégalités de santé : approches théoriques et données empiriques. Perspectives internationales : IntroductionSession 1 : Expliquer les inégalités de santé en économie et sociologieOwen O'Donnell : An Economist's Perspective on What We Know, Can Know and Need to Know About the Causes of Health InequalityOwen O'DonnellProfesseur, Erasmus University RotterdamRésuméSocioeconomic health inequality is substantial, ubiquitous and persistent. From an economics perspective, I review what is known about its causes in high-income countries and consider what can be known and needs to be known. Causal analyses have not yet delivered strong, consistent evidence that education, income and wealth impact health in adulthood, but there is evidence that cash benefits paid to low-income households often improve infant and child health outcomes. Changes in adult health have large effects on income and wealth, and childhood ill-health both persists into adulthood and constrains economic outcomes in that phase of life. What can be known about the causes of health inequality is constrained by the limited scope for causal analysis to identify effects of socioeconomic exposures that potentially take their toll on health over the life course, cumulatively and multiplicatively. To reduce health inequality, its causes need not necessarily be known, provided health policies that improve the health of the socioeconomically disadvantaged can be identified and implemented. Political support for such policies may, however, depend on knowledge (or beliefs) about the causes of health inequality.Owen O'DonnellOwen O'Donnell is Professor of Applied Economics in the School of Economics and the School of Health Policy & Management at Erasmus University Rotterdam, and a Research Fellow of the Tinbergen Institute. His research is in the field of health economics, particularly health inequality, health insurance and health behaviour. He has published in leading field journals in economics and in demography, epidemiology and medicine. He is an Editor of the Journal of Health Economics.

Mendelian Randomization Analyses Look for Causal Relationships between Oral Health and Cognitive DeclineBy Spring Hatfield, RDH, BSPHOriginal article published on Today's RDH: https://www.todaysrdh.com/mendelian-randomization-analyses-look-for-causal-relationships-between-oral-health-and-cognitive-decline/Need CE? Start earning CE credits today at ⁠⁠⁠⁠⁠https://rdh.tv/ce⁠⁠⁠⁠⁠Get daily dental hygiene articles at ⁠⁠⁠⁠⁠https://www.todaysrdh.com⁠⁠⁠⁠⁠ Follow Today's RDH on Facebook: ⁠⁠⁠⁠⁠https://www.facebook.com/TodaysRDH/⁠⁠⁠⁠⁠Follow Kara RDH on Facebook: ⁠⁠⁠⁠⁠https://www.facebook.com/DentalHygieneKaraRDH/⁠⁠⁠⁠⁠Follow Kara RDH on Instagram: ⁠⁠⁠⁠⁠https://www.instagram.com/kara_rdh/

In Hour 2, Isaac and Suke wonder why people dress so casually in the pacific northwest, congratulate the terrible Colorado Rockies for their first series sweep in over a year, and more.

Send us a text*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-y5gMRecorded 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 GenInspiring Tech Leaders - The Technology PodcastInterviews with Tech Leaders and insights on the latest emerging technology trends.Listen on: Apple Podcasts SpotifySupport the showCausal Bandits PodcastCausal AI || Causal Machine Learning || Causal Inference & DiscoveryWeb: https://causalbanditspodcast.comConnect on LinkedIn: https://www.linkedin.com/in/aleksandermolak/Join Causal Python Weekly: https://causalpython.io The Causal Book: https://amzn.to/3QhsRz4

In this exclusive interview, we sit down with Amit, Head of Machine Learning at Cloudbeds, to explore how Causal AI and Multimodal AI are revolutionizing the hotel industry. From boosting occupancy to delivering personalized guest experiences, learn how cutting-edge hotel technology is reshaping the future of hospitality. ✅ What you'll learn: What is Causal AI and how it helps hoteliers make smarter, data-driven decisions How Multimodal AI connects guest data, images, and messages for seamless operations Real examples of how AI can automate upsells, personalize stays, and break down departmental silos How Cloudbeds Intelligence is using AI to unify hotel systems and unlock new revenue opportunities

How to build artificial intelligence systems that understand cause and effect, moving beyond simple correlations? As we all know, correlation is not causation. "Spurious correlations" can show, for example, how rising ice cream sales might statistically link to more drownings, not because one causes the other, but due to an unobserved common cause like warm weather. Our guest, Utkarshani Jaimini, a researcher from the University of South Carolina's Artificial Intelligence Institute, tries to tackle this problem by using knowledge graphs that incorporate domain expertise.  Knowledge graphs (structured representations of information) are combined with neural networks in the field of neurosymbolic AI to represent and reason about complex relationships. This involves creating causal ontologies, incorporating the "weight" or strength of causal relationships and hyperrelations. This field has many practical applications such as for AI explainability, healthcare and autonomous driving. Follow our guest Utkarshani Jaimini's Webpage Linkedin Papers in focus CausalLP: Learning causal relations with weighted knowledge graph link prediction, 2024 HyperCausalLP: Causal Link Prediction using Hyper-Relational Knowledge Graph, 2024  

AI strategist James Ward breaks down why most marketing strategies fail—and how his Five Rings framework and causal AI can help agencies think, measure, and perform more effectively. SHOWPAGE: https://www.ninjacat.io/blog/wgm-podcast-rethinking-marketing-strategy-with-causal-ai  © 2025, NinjaCat 

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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Lucy and Ellie chat about large language models, chat interfaces, and causal inference. Do LLMs Act as Repositories of Causal Knowledge?: https://arxiv.org/html/2412.10635v1 Follow along on Twitter: The American Journal of Epidemiology: @AmJEpi Ellie: @EpiEllie Lucy: @LucyStats

Medial meniscal repair performed at the time of primary anterior cruciate ligament reconstruction (ACLR) has been shown to be significantly associated with subsequent surgery, and subsequent surgery has been associated with increased Knee injury and Osteoarthritis Outcome Score (KOOS) pain score and decreased patient satisfaction.   In conclusion, successful medial meniscal repair performed at the time of primary ACLR decreased clinically significant knee pain 10 years postoperatively. However, the mediating effect of subsequent surgery was significant and diminished the overall contribution of medial meniscal repair in decreasing the likelihood of KOOS pain. Continued efforts should be made to decrease the likelihood of subsequent surgery after medial meniscal repair performed at the time of primary ACLR.     Click here to read the article.

Proudly sponsored by PyMC Labs, the Bayesian Consultancy. Book a call, or get in touch!Intro to Bayes Course (first 2 lessons free)Advanced Regression Course (first 2 lessons free)Our theme music is « Good Bayesian », by Baba Brinkman (feat MC Lars and Mega Ran). Check out his awesome work!Visit our Patreon page to unlock exclusive Bayesian swag ;)Thank you to my Patrons for making this episode possible!Yusuke Saito, Avi Bryant, Ero Carrera, Giuliano Cruz, Tim Gasser, James Wade, Tradd Salvo, William Benton, James Ahloy, Robin Taylor,, Chad Scherrer, Zwelithini Tunyiswa, Bertrand Wilden, James Thompson, Stephen Oates, Gian Luca Di Tanna, Jack Wells, Matthew Maldonado, Ian Costley, Ally Salim, Larry Gill, Ian Moran, Paul Oreto, Colin Caprani, Colin Carroll, Nathaniel Burbank, Michael Osthege, Rémi Louf, Clive Edelsten, Henri Wallen, Hugo Botha, Vinh Nguyen, Marcin Elantkowski, Adam C. Smith, Will Kurt, Andrew Moskowitz, Hector Munoz, Marco Gorelli, Simon Kessell, Bradley Rode, Patrick Kelley, Rick Anderson, Casper de Bruin, Philippe Labonde, Michael Hankin, Cameron Smith, Tomáš Frýda, Ryan Wesslen, Andreas Netti, Riley King, Yoshiyuki Hamajima, Sven De Maeyer, Michael DeCrescenzo, Fergal M, Mason Yahr, Naoya Kanai, Steven Rowland, Aubrey Clayton, Jeannine Sue, Omri Har Shemesh, Scott Anthony Robson, Robert Yolken, Or Duek, Pavel Dusek, Paul Cox, Andreas Kröpelin, Raphaël R, Nicolas Rode, Gabriel Stechschulte, Arkady, Kurt TeKolste, Gergely Juhasz, Marcus Nölke, Maggi Mackintosh, Grant Pezzolesi, Avram Aelony, Joshua Meehl, Javier Sabio, Kristian Higgins, Alex Jones, Gregorio Aguilar, Matt Rosinski, Bart Trudeau, Luis Fonseca, Dante Gates, Matt Niccolls, Maksim Kuznecov, Michael Thomas, Luke Gorrie, Cory Kiser, Julio, Edvin Saveljev, Frederick Ayala, Jeffrey Powell, Gal Kampel, Adan Romero, Will Geary, Blake Walters, Jonathan Morgan, Francesco Madrisotti, Ivy Huang, Gary Clarke, Robert Flannery, Rasmus Hindström, Stefan, Corey Abshire, Mike Loncaric, David McCormick, Ronald Legere, Sergio Dolia and Michael Cao.Takeaways:Sharks play a crucial role in maintaining healthy ocean ecosystems.Bayesian statistics are particularly useful in data-poor environments like ecology.Teaching Bayesian statistics requires a shift in mindset from traditional statistical methods.The shark meat trade is significant and often overlooked.Ray meat trade is as large as shark meat trade, with specific markets dominating.Understanding the ecological roles of species is essential for effective conservation.Causal language is important in ecological research and should be encouraged.Evidence-driven decision-making is crucial in balancing human and ecological needs.Expert opinions are...

In this episode of Peace of Mind, hosts John and Eoin look at Causal depression which is the result of a traumatic event and ways to deal with it and ways that do not help but make matters worse. L'articolo E64 | Peace of Mind – John and Eoin – Causal depression and how to deal with it proviene da Radio Maria.

In this episode of Daily Value, we look at the latest research on B vitamins and their in neuropsychiatric disorders. A newly published meta-analysis suggests a causal relationship between B vitamin deficiencies and neuropsychiatric disorders. We will break down the scientific findings on B6, B12, and folate, shedding light on their roles in conditions like Parkinson's disease.Discussion Points:How recent genetic studies support a causal link between B vitamin deficiencies and mental health conditions.The role of B vitamins in reducing neurotoxicity and slowing brain atrophy.How vitamin B12 may protect against dopamine neuron loss and disease progression.Evidence linking low B6 levels to neurotransmitter imbalances and schizophrenia risk.The impact of folate on one-carbon metabolism and its protective role in neurodegeneration.https://www.sciencedirect.com/science/article/abs/pii/S0149763425000685#:~:text=In a meta-analysis of,beneficial for certain specific diseases.https://pubmed.ncbi.nlm.nih.gov/26757190/https://pubmed.ncbi.nlm.nih.gov/32257364/https://pubmed.ncbi.nlm.nih.gov/30858560/https://pubmed.ncbi.nlm.nih.gov/32424116/https://pubmed.ncbi.nlm.nih.gov/33941768/Support the show

In this Episode:Catholic Teaching and Final Salvation Diocesan Staff Apologist and Speaker for Catholic Answers, Dr. Karlo Broussard, explains the Why's behind Catholic Beliefs from Faith, Morality, and Culture. Providing the Reasons behind the claims made by the Catholic Church. Send your questions to...Karlo@stmichaelradio.comA Production of St. Michael Catholic RadioThe Catholic Reason Airs Every Thursday on 94.9 St Michael Catholic Radio at 4 p.m. CST.

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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

On this episode, host Sima Vasa talks to George London, Chief Technology Officer of Upwave. George shares insights into the complexities of the advertising ecosystem, the role of data in campaign optimization and the parallels between financial and ad markets. Key Takeaways: (02:14) From philosophy to data leadership.(05:34) Persistence and adaptability shaped George's path to CTO.(08:15) Causal inference helps Upwave provide reliable insights for large media investments.(11:32) Rigorous measurement turns $10M in ad spend into $20M in value.(13:10) Daily insights from Upwave simplify complex national ad campaigns.(15:12) Actionable insights drive Upwave's mission to optimize brand investments.(16:23) Targeted surveys reveal brand impact across specific ad campaigns.(17:59) The ad ecosystem spans brands, publishers and a complex chain of intermediaries.(20:36) Generative AI powers Upwave's automated ad reporting with Trade Desk.(22:47) Advertising shares dynamics with financial markets, including bidding and price discovery. Resources Mentioned: UpWaveTrade Desk Thanks for listening to the Data Gurus podcast, brought to you by Infinity Squared. If you enjoyed this episode, please leave a 5-star review to help get the word out about the show, and be sure to subscribe so you never miss another insightful conversation. #Analytics #MA #Data #Strategy #Innovation #Acquisitions #MRX #Restech

After his recent trip to Atlanta for a speaking gig, Keith's got some fresh insights about how the way we see ourselves can really shape our success. He's breaking down the difference between a scarcity mindset that holds you back and an abundant mindset that drives success. Keith shares some personal stories and drops some knowledge bombs on how shifting your identity and aligning with the right energy can totally change your game. If you want to level up in business or life, this episode is packed with tips and wisdom you won't want to miss. Let's dive in!   Check out these episode highlights: 00:00 -  Overcoming Self-Limiting Beliefs and Burnout 06:03 -  Spiritual Alignment Attracts Positive Opportunities 07:53 - Elevating Identity to Overcome Scarcity Mindset 11:12 - "Exploring Purpose and Seeking Guidance" 14:41 - "Embrace Clarity Through Higher Perspective" Key Takeaways:  Identity Dictates Perception: The same piece of information can lead to vastly different outcomes depending on one's mindset. Whether you're stuck in a scarcity mindset or thriving in an abundance-focused one, your identity steers the ship. The Power of Conviction: Aligning with high beliefs and developing strong convictions in your actions can attract the right people and opportunities. Conviction is an unparalleled influence that can't be easily replicated. Faith and Action: Faith isn't just belief; it's action. It's about courage and the commitment to move forward, trusting in your ability to figure things out. This approach opens up new perspectives and higher levels of opportunity.   Resources and Websites: 

Proudly sponsored by PyMC Labs, the Bayesian Consultancy. Book a call, or get in touch!My Intuitive Bayes Online Courses1:1 Mentorship with meOur theme music is « Good Bayesian », by Baba Brinkman (feat MC Lars and Mega Ran). Check out his awesome work!Visit our Patreon page to unlock exclusive Bayesian swag ;)Takeaways:Bayesian statistics offers a robust framework for econometric modeling.State space models provide a comprehensive way to understand time series data.Gaussian random walks serve as a foundational model in time series analysis.Innovations represent external shocks that can significantly impact forecasts.Understanding the assumptions behind models is key to effective forecasting.Complex models are not always better; simplicity can be powerful.Forecasting requires careful consideration of potential disruptions. Understanding observed and hidden states is crucial in modeling.Latent abilities can be modeled as Gaussian random walks.State space models can be highly flexible and diverse.Composability allows for the integration of different model components.Trends in time series should reflect real-world dynamics.Seasonality can be captured through Fourier bases.AR components help model residuals in time series data.Exogenous regression components can enhance state space models.Causal analysis in time series often involves interventions and counterfactuals.Time-varying regression allows for dynamic relationships between variables.Kalman filters were originally developed for tracking rockets in space.The Kalman filter iteratively updates beliefs based on new data.Missing data can be treated as hidden states in the Kalman filter framework.The Kalman filter is a practical application of Bayes' theorem in a sequential context.Understanding the dynamics of systems is crucial for effective modeling.The state space module in PyMC simplifies complex time series modeling tasks.Chapters:00:00 Introduction to Jesse Krabowski and Time Series Analysis04:33 Jesse's Journey into Bayesian Statistics10:51 Exploring State Space Models18:28 Understanding State Space Models and Their Components

reference: Sri Aurobindo and the Mother, Powers Within, Chapter XIX Occult Powers of the Subliminal, pp. 151-152 This episode is also available as a blog post at https://sriaurobindostudies.wordpress.com/2025/01/18/the-quest-to-establish-the-connection-to-the-higher-subtler-causal-planes-of-existence/ Video presentations, interviews and podcast episodes are all available on the YouTube Channel https://www.youtube.com/@santoshkrinsky871 More information about Sri Aurobindo can be found at www.aurobindo.net  The US editions and links to e-book editions of Sri Aurobindo's writings can be found at Lotus Press www.lotuspress.com

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

New Casual Catch-Up episode is now available! In this episode, we discuss our recent trip to Philadelphia for PAX Unplugged. You'll also hear from the designers and developers of several stand-out SM:TT games that we spoke with at the con.

Proudly sponsored by PyMC Labs, the Bayesian Consultancy. Book a call, or get in touch!My Intuitive Bayes Online Courses1:1 Mentorship with meOur theme music is « Good Bayesian », by Baba Brinkman (feat MC Lars and Mega Ran). Check out his awesome work!Visit our Patreon page to unlock exclusive Bayesian swag ;)Takeaways:CFA is commonly used in psychometrics to validate theoretical constructs.Theoretical structure is crucial in confirmatory factor analysis.Bayesian approaches offer flexibility in modeling complex relationships.Model validation involves both global and local fit measures.Sensitivity analysis is vital in Bayesian modeling to avoid skewed results.Complex models should be justified by their ability to answer specific questions.The choice of model complexity should balance fit and theoretical relevance. Fitting models to real data builds confidence in their validity.Divergences in model fitting indicate potential issues with model specification.Factor analysis can help clarify causal relationships between variables.Survey data is a valuable resource for understanding complex phenomena.Philosophical training enhances logical reasoning in data science.Causal inference is increasingly recognized in industry applications.Effective communication is essential for data scientists.Understanding confounding is crucial for accurate modeling.Chapters:10:11 Understanding Structural Equation Modeling (SEM) and Confirmatory Factor Analysis (CFA)20:11 Application of SEM and CFA in HR Analytics30:10 Challenges and Advantages of Bayesian Approaches in SEM and CFA33:58 Evaluating Bayesian Models39:50 Challenges in Model Building44:15 Causal Relationships in SEM and CFA49:01 Practical Applications of SEM and CFA51:47 Influence of Philosophy on Data Science54:51 Designing Models with Confounding in Mind57:39 Future Trends in Causal Inference01:00:03 Advice for Aspiring Data Scientists01:02:48 Future Research DirectionsThank you to my Patrons for making this episode possible!Yusuke Saito, Avi Bryant, Ero Carrera, Giuliano Cruz, Tim Gasser, James Wade, Tradd Salvo, William Benton, James Ahloy,

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)=============================

Scott Hebner, an experienced AI analyst and former IBM executive, brings a wealth of knowledge on the rapidly evolving field of artificial intelligence. After transitioning from a corporate role to advisory work, Hebner has focused on how AI is transforming industries, particularly small businesses. He highlights the shift from traditional predictive AI models to powerful generative AI tools that are democratizing access to cutting-edge technology. Small businesses, in particular, are benefiting from AI's ability to streamline operations, improve decision-making, and increase productivity, leveling the playing field with larger enterprises. As AI continues to evolve, the next big leap is goal-oriented AI agents—systems that autonomously pursue objectives and solve complex business problems. Hebner emphasizes how businesses must adapt quickly to this fast-paced development and invest in AI talent to stay competitive. The demand for skilled AI professionals is growing, but the supply is lagging, creating an urgent need for investments in education and training to build the workforce of the future. The intersection of AI and decision-making is also transforming industries, with causal AI emerging as a powerful tool for understanding cause and effect in business scenarios. To stay up-to-date on the latest AI trends and developments, follow Scott Hebner and explore theCUBE Research newsletters for in-depth and expert insights on the cutting edge of AI and technology.  Whether you're a small business owner or a tech enthusiast, these resources are a great way to stay informed and ahead of the curve in the AI revolution. We're happy you're here! Like the pod? Visit our website! Start your trial on Simplified. Schedule a consult, get on the mailing list, and learn more about my favorite tools and programs via https://www.yourbrandamplified.com

Do language models understand the causal structure of the world, or do they merely note correlations? And what happens when you build a big AI society out of them? In this brief episode, recorded at the Bay Area Alignment Workshop, I chat with Zhijing Jin about her research on these questions. Patreon: https://www.patreon.com/axrpodcast Ko-fi: https://ko-fi.com/axrpodcast The transcript: https://axrp.net/episode/2024/11/14/episode-38_0-zhijing-jin-llms-causality-multi-agent-systems.html FAR.AI: https://far.ai/ FAR.AI on X (aka Twitter): https://x.com/farairesearch FAR.AI on YouTube: https://www.youtube.com/@FARAIResearch The Alignment Workshop: https://www.alignment-workshop.com/   Topics we discuss, and timestamps: 00:35 - How the Alignment Workshop is 00:47 - How Zhijing got interested in causality and natural language processing 03:14 - Causality and alignment 06:21 - Causality without randomness 10:07 - Causal abstraction 11:42 - Why LLM causal reasoning? 13:20 - Understanding LLM causal reasoning 16:33 - Multi-agent systems   Links: Zhijing's website: https://zhijing-jin.com/fantasy/ Zhijing on X (aka Twitter): https://x.com/zhijingjin Can Large Language Models Infer Causation from Correlation?: https://arxiv.org/abs/2306.05836 Cooperate or Collapse: Emergence of Sustainable Cooperation in a Society of LLM Agents: https://arxiv.org/abs/2404.16698   Episode art by Hamish Doodles: hamishdoodles.com

How often do you assume you know WHY something happened? How quickly do you land on a conclusion about why something DIDN'T happen? Turns out, we humans are great at assigning "causes" to things. We're just not great at getting it right! In this quick episode, I clue you into the cognitive tendency of Causal Attribution, including where it comes from, how to spot it, and how to make conscious decisions in spite of it! LINKS! What's your DECISION STYLE? Take the Quiz! Need to say NO? Here's your 11-minute Crash Course on Saying NO Guilt-Free. Ready to talk about getting clear, intentional & sure of your choices? Book your free consultation Join the Alignment and Accountability Club Hire me for two weeks to consciously, confidently work through a decision with the Make a Decision Package

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

There is a growing mandate among researchers and VCs to provide proof of causal human biology for new targets. On the latest BioCentury This Week podcast, BioCentury's editors discuss the different strategies being deployed to identify causal links to disease using observational patient data or human cell models, including the challenges that come with each approach and the various computational methodologies companies are using.They also discuss the outcome of FDA's advisory committee meeting on Barth syndrome candidate elamipretide from Stealth Biotherapeutics, and the implications of the discussion for review of ultrarare disease therapies more broadly.Diving into the deal of the day, the editors review the proposal by H. Lundbeck to acquire Longboard Pharmaceuticals for $2.6 billion, and discuss how the biotech's therapy for developmental epilepsies may stack up against competitors.View full story: https://www.biocentury.com/article/65384300:00 - Introduction00:34 - Causal Biology and Big Data17:52 - FDA's Ultra-Rare Decision27:29 - Lundbeck Acquires LongboardTo submit a question to BioCentury's editors, email the BioCentury This Week team at podcasts@biocentury.com.Reach us by sending a text

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

If you seek a compelling exploration of contemporary armed conflict, then Conflict Realism: Understanding the Causal Logic of Modern War and Warfare (Howgate Publishing, 2024) by Amos C. Fox is for you. It delves into the intricate web of causation to unveil five pivotal trends shaping the landscape of war and warfare - urban warfare, sieges, attrition, precision strike strategy, and proxy wars - revealing a stark reality: wars remain far more attritional than anticipated by policymakers, military practitioners, and analysts alike. What's more, just as attritional wars are becoming quite common, conflict elongation – wars of extended duration – are also becoming the norm. Through insightful analysis and a keen understanding of geopolitical intricacies, Amos Fox navigates the reader through the intricate interplay of these trends, shedding light on their profound implications for global security. This riveting work challenges conventional wisdom, offering readers a thought-provoking perspective on the contemporary nature of armed conflicts, ultimately urging a reconsideration of strategies and policies in the face of an ever-evolving battlefield. Amos C. Fox, PhD, is a Fellow with Arizona State University's Future Security Initiative. Amos is also a lecturer in the Department of Political Science at the University of Houston. He hosts the Revolution in Military Affairs, Soldier Pulse and WarCast podcasts, serves as an editorial board member with the Journal of Military Studies and is a senior editor with Small Wars Journal. Amos is also a retired US Army officer, where he served more than 24 years, retiring at the rank of Lieutenant Colonel. Stephen Satkiewicz is an independent scholar whose research areas are related to Civilizational Sciences, Social Complexity, Big History, Historical Sociology, military history, War studies, International Relations, Geopolitics, as well as Russian and East European history. Learn more about your ad choices. Visit megaphone.fm/adchoices Support our show by becoming a premium member! https://newbooksnetwork.supportingcast.fm/new-books-network

If you seek a compelling exploration of contemporary armed conflict, then Conflict Realism: Understanding the Causal Logic of Modern War and Warfare (Howgate Publishing, 2024) by Amos C. Fox is for you. It delves into the intricate web of causation to unveil five pivotal trends shaping the landscape of war and warfare - urban warfare, sieges, attrition, precision strike strategy, and proxy wars - revealing a stark reality: wars remain far more attritional than anticipated by policymakers, military practitioners, and analysts alike. What's more, just as attritional wars are becoming quite common, conflict elongation – wars of extended duration – are also becoming the norm. Through insightful analysis and a keen understanding of geopolitical intricacies, Amos Fox navigates the reader through the intricate interplay of these trends, shedding light on their profound implications for global security. This riveting work challenges conventional wisdom, offering readers a thought-provoking perspective on the contemporary nature of armed conflicts, ultimately urging a reconsideration of strategies and policies in the face of an ever-evolving battlefield. Amos C. Fox, PhD, is a Fellow with Arizona State University's Future Security Initiative. Amos is also a lecturer in the Department of Political Science at the University of Houston. He hosts the Revolution in Military Affairs, Soldier Pulse and WarCast podcasts, serves as an editorial board member with the Journal of Military Studies and is a senior editor with Small Wars Journal. Amos is also a retired US Army officer, where he served more than 24 years, retiring at the rank of Lieutenant Colonel. Stephen Satkiewicz is an independent scholar whose research areas are related to Civilizational Sciences, Social Complexity, Big History, Historical Sociology, military history, War studies, International Relations, Geopolitics, as well as Russian and East European history. Learn more about your ad choices. Visit megaphone.fm/adchoices Support our show by becoming a premium member! https://newbooksnetwork.supportingcast.fm/military-history

If you seek a compelling exploration of contemporary armed conflict, then Conflict Realism: Understanding the Causal Logic of Modern War and Warfare (Howgate Publishing, 2024) by Amos C. Fox is for you. It delves into the intricate web of causation to unveil five pivotal trends shaping the landscape of war and warfare - urban warfare, sieges, attrition, precision strike strategy, and proxy wars - revealing a stark reality: wars remain far more attritional than anticipated by policymakers, military practitioners, and analysts alike. What's more, just as attritional wars are becoming quite common, conflict elongation – wars of extended duration – are also becoming the norm. Through insightful analysis and a keen understanding of geopolitical intricacies, Amos Fox navigates the reader through the intricate interplay of these trends, shedding light on their profound implications for global security. This riveting work challenges conventional wisdom, offering readers a thought-provoking perspective on the contemporary nature of armed conflicts, ultimately urging a reconsideration of strategies and policies in the face of an ever-evolving battlefield. Amos C. Fox, PhD, is a Fellow with Arizona State University's Future Security Initiative. Amos is also a lecturer in the Department of Political Science at the University of Houston. He hosts the Revolution in Military Affairs, Soldier Pulse and WarCast podcasts, serves as an editorial board member with the Journal of Military Studies and is a senior editor with Small Wars Journal. Amos is also a retired US Army officer, where he served more than 24 years, retiring at the rank of Lieutenant Colonel. Stephen Satkiewicz is an independent scholar whose research areas are related to Civilizational Sciences, Social Complexity, Big History, Historical Sociology, military history, War studies, International Relations, Geopolitics, as well as Russian and East European history. Learn more about your ad choices. Visit megaphone.fm/adchoices Support our show by becoming a premium member! https://newbooksnetwork.supportingcast.fm/political-science

If you seek a compelling exploration of contemporary armed conflict, then Conflict Realism: Understanding the Causal Logic of Modern War and Warfare (Howgate Publishing, 2024) by Amos C. Fox is for you. It delves into the intricate web of causation to unveil five pivotal trends shaping the landscape of war and warfare - urban warfare, sieges, attrition, precision strike strategy, and proxy wars - revealing a stark reality: wars remain far more attritional than anticipated by policymakers, military practitioners, and analysts alike. What's more, just as attritional wars are becoming quite common, conflict elongation – wars of extended duration – are also becoming the norm. Through insightful analysis and a keen understanding of geopolitical intricacies, Amos Fox navigates the reader through the intricate interplay of these trends, shedding light on their profound implications for global security. This riveting work challenges conventional wisdom, offering readers a thought-provoking perspective on the contemporary nature of armed conflicts, ultimately urging a reconsideration of strategies and policies in the face of an ever-evolving battlefield. Amos C. Fox, PhD, is a Fellow with Arizona State University's Future Security Initiative. Amos is also a lecturer in the Department of Political Science at the University of Houston. He hosts the Revolution in Military Affairs, Soldier Pulse and WarCast podcasts, serves as an editorial board member with the Journal of Military Studies and is a senior editor with Small Wars Journal. Amos is also a retired US Army officer, where he served more than 24 years, retiring at the rank of Lieutenant Colonel. Stephen Satkiewicz is an independent scholar whose research areas are related to Civilizational Sciences, Social Complexity, Big History, Historical Sociology, military history, War studies, International Relations, Geopolitics, as well as Russian and East European history. Learn more about your ad choices. Visit megaphone.fm/adchoices Support our show by becoming a premium member! https://newbooksnetwork.supportingcast.fm/world-affairs

If you seek a compelling exploration of contemporary armed conflict, then Conflict Realism: Understanding the Causal Logic of Modern War and Warfare (Howgate Publishing, 2024) by Amos C. Fox is for you. It delves into the intricate web of causation to unveil five pivotal trends shaping the landscape of war and warfare - urban warfare, sieges, attrition, precision strike strategy, and proxy wars - revealing a stark reality: wars remain far more attritional than anticipated by policymakers, military practitioners, and analysts alike. What's more, just as attritional wars are becoming quite common, conflict elongation – wars of extended duration – are also becoming the norm. Through insightful analysis and a keen understanding of geopolitical intricacies, Amos Fox navigates the reader through the intricate interplay of these trends, shedding light on their profound implications for global security. This riveting work challenges conventional wisdom, offering readers a thought-provoking perspective on the contemporary nature of armed conflicts, ultimately urging a reconsideration of strategies and policies in the face of an ever-evolving battlefield. Amos C. Fox, PhD, is a Fellow with Arizona State University's Future Security Initiative. Amos is also a lecturer in the Department of Political Science at the University of Houston. He hosts the Revolution in Military Affairs, Soldier Pulse and WarCast podcasts, serves as an editorial board member with the Journal of Military Studies and is a senior editor with Small Wars Journal. Amos is also a retired US Army officer, where he served more than 24 years, retiring at the rank of Lieutenant Colonel. Stephen Satkiewicz is an independent scholar whose research areas are related to Civilizational Sciences, Social Complexity, Big History, Historical Sociology, military history, War studies, International Relations, Geopolitics, as well as Russian and East European history. Learn more about your ad choices. Visit megaphone.fm/adchoices Support our show by becoming a premium member! https://newbooksnetwork.supportingcast.fm/national-security

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

Welcome back to the Stronger Bones Lifestyle Podcast! In Episode 79, your host Debi Robinson dives deep into the topic of bone health, specifically focusing on osteopenia and how it differs from osteoporosis. Drawing from her extensive experience working with women through yoga classes, online programs, and direct consultations, Debi sheds light on these often misunderstood conditions.Debi embarks on a detailed exploration of how bone loss begins long before most women are aware, starting around age 35. She explains the importance of understanding bone health terminology, the metabolic processes contributing to bone loss, and the role of lifestyle factors such as diet, stress, and exercise. By dissecting statistics and providing practical insights, Debi empowers her listeners to take control of their bone health.Listen to learn more about the stages of bone loss, the central role of calcium and vitamin D, and the statistics regarding bone health in postmenopausal women. Debi emphasizes that osteoporosis and osteopenia are not inevitable parts of aging and offers actionable steps for prevention and reversal through lifestyle adjustments.This episode provides a comprehensive guide for women looking to understand and improve their bone health, arming them with the knowledge needed to live a stronger, more empowered life.Key  Takeaways:[1:54] Osteopenia 101[2:46] Building our bones and loosing bones[4:16] Acidic Blood[5:42] First Stage of Bone Loss[7:02] The Stats of Bone Loss[8:31] The disease state of Bone Loss [8:54] What to look at in your DEXA scan[9:45] How the term Osteopenia came about[10:56] Being in control of our bone health[12:27] Metabolic and biochemical components[13:57] Medications[14:41] Causal factors of OsteopeniaMemorable Quotes:"It's not because you're not taking enough Calcium and Vitamin D supplements, it's because your body is not properly digesting and absorbing those nutrients." [6:02] - Debi"We look at bone loss as a disease and there's a pill for disease. Well that's not necessarily true." [14:04] - Debi"You are in control of you, so learn what you can do to build stronger bones." [20:28] - DebiTo learn more about me and to stay connected, click on the links below:Instagram: @debirobinsonwellnessWebsite: DebiRobinson.comHealthy Gut Healthy Bones Program 

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

http://www.coachmiked.com Subscribe: Apple Podcasts: https://podcasts.apple.com/us/podcast/impact-and-fulfillment-with-coach-mike-d/id1230596918 Spotify: https://open.spotify.com/show/4oNGxgmdwaTOpWwdf1BGyR YouTube: https://www.youtube.com/@coachmikeD Be Blessed, Well and Wise! -Coach Mike D

WATCH: https://youtu.be/_Kj2OgkxGa0 Terrence Deacon is Professor and Chair of the Department of Anthropology and member of the Helen Wills Neuroscience Institute at the University of California, Berkeley. His research combines developmental evolutionary biology and comparative neuroanatomy to investigate the evolution of human cognition, and is particularly focused on the explanation of emergent processes in biology and cognition. Terrence is a Harvard Lehman Fellow, a Harvard Medical School Psychiatric Neuroscience Fellow, a Western Washington University Centenary Alumni Fellow, and the 69^th James Arthur Lecturer for the American Museum of Natural History. He has published over 100 research papers in collected volumes and scholarly journals, and his acclaimed book, "The Symbolic Species: The Co-evolution of Language and the Brain" (1997) was awarded the I. J. Staley Prize for the most influential book in Anthropology in 2005 by the School of American Research. His other books include "Incomplete Nature: How Mind Emerged from Matter" (2011) and "Homo Sapiens: Evolutionary Biology and the Human Sciences" (2012). TIMESTAMPS: (0:00) - Introduction (1:29) - The Mind-Body Problem (10:50) - Universal Grammar (18:10) - Linguistic Prosthesis & Shared Cognition (27:49) - Teleology vs Teleonomy (35:29) - Absential Causal Powers (39:58) - Thermodynamics, Morphodynamics & Teleodynamics (44:20) - The Role of Constraints & Causal Emergence (1:00:55) - Self-Organization, Self-Assembly & Self-Repair (1:24:17) - The Origin of Life on Earth & Proto-Life in the Cosmos (1:33:50) - Pre-LUCA (Last Universal Common Ancestor) Evolution Problem (1:46:45) - "Falling Up: How Inverse Darwinism Catalyzes Evolution" (Terrence's Next Book) (2:06:50) - Incomplete Nature & Mind's Emergence (2:20:06) - Mind-Body Solution & Landscape of Consciousness (2:28:06) - Implications of Terrence's Work (2:37:10) - Artificial Intelligence (2:44:30) - Terrence's Major Influences (Peirce etc.) (3:01:30) - Importance of Development in Evolution ("EvoDevo") (3:06:40) - Conclusion EPISODE LINKS: - Terrence's Website: https://tinyurl.com/2zchenan - Terrence's Publications: https://tinyurl.com/4tctx9ve - Terrence's Books: https://tinyurl.com/yrxt72dh - Keith Frankish: https://youtu.be/jTO-A1lw4JM - Michael Levin: https://youtu.be/1R-tdscgxu4 - Mark Solms: https://youtu.be/rkbeaxjAZm4 CONNECT: - Website: https://tevinnaidu.com - Podcast: https://podcasters.spotify.com/pod/show/drtevinnaidu - Twitter: https://twitter.com/drtevinnaidu - Facebook: https://www.facebook.com/drtevinnaidu - Instagram: https://www.instagram.com/drtevinnaidu - LinkedIn: https://www.linkedin.com/in/drtevinnaidu ============================= Disclaimer: The information provided on this channel is for educational purposes only. The content is shared in the spirit of open discourse and does not constitute, nor does it substitute, professional or medical advice. We do not accept any liability for any loss or damage incurred from you acting or not acting as a result of listening/watching any of our contents. You acknowledge that you use the information provided at your own risk. Listeners/viewers are advised to conduct their own research and consult with their own experts in the respective fields.

PhaseV is pioneering the use of reinforcement learning, causal ML, and adaptive trial design to optimize resource utilization and time efficiency in drug development.