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Moral theologian Fr. Thomas Berg and philosopher and therapist Dr. Andrea Messineo take on the topic of personal conscience and parts work through a Catholic lens. We explore the relationships among conscience, parts, the innermost self, the intellect, the will, impulses, and desires. We address concupiscence and parts, and offer specific examples. Join us for a fascinating exploration of conscience and parts. Check out these other episodes:https://youtu.be/bw-zUp2h_TAhttps://youtu.be/f5MNCaCJLychttps://youtu.be/Isxmlx8pQAsDr. Peter's advanced group for Catholic formators: Relating Wholeheartedly with God in Prayer, Mondays from 10:00 AM to 11:30 AM Eastern starting on March 9, 2026. Find out more here: https://members.soulsandhearts.com/registrationDr. Gerry's advanced group for Catholic formators: Surviving, Healing, Thriving, and Flourishing - A Path To Greater Integration Wednesdays from 10:00 AM to 11:30 AM Eastern, starting on March 11, 2026Fr. Thomas Berg's books: Hurting in the Church: A Way Forward for Wounded Catholics: https://www.barnesandnoble.com/w/hurting-in-the-church-fr-thomas-berg/1124597873Choosing Forgiveness: Unleash the Power of God's Grace: https://www.barnesandnoble.com/w/choosing-forgiveness-fr-thomas-berg/1140395384?ean=9781681926537Dr. Andrea Messineo's book, Alone in Church: https://www.amazon.com/ALONE-CHURCH-Andrea-Messineo/dp/1732054290Check out Dr. Messineo's website at andreamessineolpcc.comKey moments:16:15 What are the relationships among one's innermost self, one's parts, and one's conscience?21:25 St. Thomas Aquinas' emphasis on prudence23:30 How parts with emotions have a role in a well-formed conscience and the innermost self does not have a “localized omniscience.” 31:30 What are the relationships among parts and the faculties of the intellect and the will?37:00 Parts are closely connected with impulses and desires, driving agendas40:00 What about addictions, obsessions, and compulsions?45:40 Can a person possess a virtue, but parts of that person not have access to that virtue?56:20 Does the innermost self need any formation from others, or is it complete, as Richard Schwartz maintains?1:08:00 Causal chains that lead to morally problematic behaviors1:17:20 What is concupiscence and does it always need to be lodged in a part?
In this episode of Science of Reading: The Podcast, Susan Lambert is joined by Kristen McMaster, Ph.D., Guy Bond Chair in Reading and professor of special education in the Department of Educational Psychology at University of Minnesota. Together, they explore how reading comprehension isn't just about what's on the page—it's also about what's not there—and share practical insights on how to support students in developing inference skills. Susan and Kristen also discuss the dual processes of activation and integration when making inferences; the distinction between teaching students to process text actively versus teaching students to apply comprehension strategies; and different types of inferences, including causal, bridging, and elaborative.Show notes:Submit your questions to our listener mailbagAccess free, high-quality resources—including our recent Science of Reading: The Podcast Essentials "Comprehension" episode—at our companion professional learning page Download our Comprehension 101 bundle: Access free comprehension resources, including e-books, and on-demand professional learningConnect with Kristen McMasterLearn more about Kristen McMasterListen to Season 2 of Amplify's Beyond My Years podcastJoin our community Facebook groupConnect with Susan LambertQuotes:"Inferencing is really central to comprehension. We wouldn't comprehend if we didn't make inferences." —Kristen McMaster"I would encourage teachers not to underestimate the importance of supporting even the inferences that might seem obvious to us." —Kristen McMaster"Good comprehenders are often making very automatic inferences that they don't even realize." —Kristen McMaster"It helps to explicitly teach what an inference is in language that students will understand." —Kristen McMasterTimestamps*:00:00 Introduction: Filling in the gaps with inferences, with Kristen McMaster, Ph.D.05:00 Comprehension is how we make sense of the world around us09:00 The types of inferences: Causal, bridging, elaborative, and theory of mind17:00 How teachers can help students develop inference skills22:00 Creating an effective questioning strategy27:00 How teachers can preview a text and think about the inferences that might need to be made31:00 Supporting students who process texts in different ways37:00 The timing of comprehension questions40:00 The connection between oral language comprehension and text comprehension45:00 Final thought: Teacher's shouldn't underestimate the importance of inferences that might seem obvious.*Timestamps are approximate, rounded to nearest minute
What's up folks, today we have the pleasure of sitting down with Anthony Rotio, Chief Data Strategy Officer at GrowthLoop.(00:00) - Intro (01:10) - In this episode (04:05) - Journeying From Robotics to Modern Marketing Systems (11:05) - Most Marketing Systems Don't Learn Because They Lack Feedback Loops (16:10) - The Martech Engineering Talent Gap (19:51) - AI Will Amplify Whoever Has the Cleanest Causal Feedback Loop (29:17) - Agent Context Graphs for Drift Detection in Marketing Systems (31:51) - Humans Will Set Hypotheses, AI Will Accelerates Iteration (35:50) - The Evolution of Retail Media Networks (45:07) - How Commerce Networks Redefine Targeting With Governed Data (48:26) - How Agent to Agent Commerce Operates Inside Marketing Funnels (53:04) - Google Universal Commerce Protocol Explained (54:43) - Personal Happiness System (56:30) - Favorite Books Summary: Anthony traces a path from robotics and computer science to his current role where he approaches marketing as an engineering system. He explains how execution-first marketing stacks weaken feedback loops and fragment data, which slows learning and iteration. He introduces the agent context graph as a causality model that lets AI simulate and predict customer behavior with greater confidence. The conversation also covers retail media networks, first-party data monetization through governed access, and a shift toward zero-to-zero marketing driven by agent-to-agent transactions. He closes by stressing that strong data foundations determine who can compete as marketing becomes more automated and agent-driven.About AnthonyAnthony Rotio is the Chief Data Strategy Officer at GrowthLoop, where he leads partnerships and builds generative AI product features for marketers, including multi-agent systems, AI-driven audience building, and benchmarking and evaluation work. He previously served as GrowthLoop's Chief Customer Officer, where he built and led teams across data engineering, data science, and solutions architecture while supporting product development and strategic sales efforts.Before GrowthLoop, Anthony spent nearly six years at AB InBev, where he led a $100M owned retail business unit with full P&L responsibility and drove major growth through operational and digital transformation work. He also led U.S. marketing for Budweiser, Bud Light, Michelob Ultra, Stella Artois, and other brands across music, food, and related consumer programs. He earned a B.A. in computer science from Harvard, played linebacker on the Harvard football team, founded the consumer product Pizza Shelf, and holds a Google Professional Cloud Architect certification.Journeying From Robotics to Modern Marketing SystemsAnthony's career started far away from marketing. He trained as a computer scientist and spent his early years working with robotics and reinforcement learning. His first exposure to a learning agent left a lasting impression because the system behaved less like traditional software and more like something adaptive. That experience shaped how he would later think about work, systems, and feedback. He learned early that progress comes from loops that learn, not static instructions.That mindset followed him into an unexpected chapter at AB InBev. Anthony entered a world defined by scale, brands, and operational complexity. He treated his technical background like a carpenter treats tools, useful only when applied to real problems. Running marketing across major beer brands taught him how value is created inside large organizations. It also exposed a recurring issue. Marketing teams had ambition and data, but execution moved slowly because ideas had to travel through layers of translation before anything reached customers.That friction became impossible to ignore. Audience definitions moved through tickets. Campaigns waited on queries. Data teams became bottlenecks through no fault of their own. Anthony felt the pull back toward technology, where systems could shorten the distance between intent and action. That pull led him to GrowthLoop, where he joined early and worked directly with customers. The appeal was immediate. The product connected straight to cloud data and removed several layers of mediation that marketing teams had accepted as normal.As language models improved, Anthony recognized a familiar pattern. Audience building behaved like a translation problem. Marketers described people and intent in natural language, while systems demanded structured logic. Early experiments showed that natural language models could close that gap. Anthony framed the idea clearly.“Audience building is a translation problem. You start with a business idea and you end with a query on top of data.”Momentum followed quickly. Customers like Indeed and Google responded because speed changed behavior. Teams experimented more often and refined audiences based on results instead of assumptions. Conversations with Sam Altman and collaboration with OpenAI reinforced that this capability belonged in the core workflow. Standing on stage at Google Cloud Next marked a clear moment of validation.That arc reshaped Anthony's role into Chief Data Strategy Officer. His work now focuses on building systems that learn over time. Faster audience creation leads to shorter feedback loops. Shorter loops improve decision quality. Better decisions compound. The throughline from robotics to marketing holds steady. Systems improve when learning sits at the center of execution.Key takeaway: Career leverage often comes from carrying one mental model across multiple domains. Anthony applied learning systems thinking from computer science to enterprise marketing, then rebuilt the tooling to match that mindset. You can do the same by identifying where translation slows your work, then designing interfaces that move intent directly into action. When feedback loops tighten, progress accelerates naturally.Most Marketing Systems Don't Learn Because They Lack Feedback LoopsMarketing organizations generate enormous amounts of activity, but learning often lags behind execution. Campaigns launch on schedule, dashboards fill with numbers, and post-campaign reviews happen right on time. The pattern repeats month after month with small adjustments and familiar explanations. Over time, teams become highly efficient at producing output while remaining surprisingly weak at retaining knowledge. The system rewards motion, visibility, and short-term lifts, which slowly conditions teams to forget what they learned last quarter.Anthony connects this behavior to structural pressure inside large organizations. Quarterly reporting cycles dominate priorities, and executive tenures continue to compress. Leaders feel urgency to show impact quickly and publicly. Compounding growth requires early patience and repeated reinforcement, which rarely aligns with board expectations or career incentives. Short time horizons shape long-term behavior, even when everyone agrees that learning should stack over time.“When you think about compound interest in finance, the early part looks almost linear. People want big bumps now, even if those bumps never build momentum.”Technology choices deepen the problem. Many companies invested heavily in customer data and built impressive data clouds that capture transactions, events, and engagement in detail. Activation remains slow because teams still rely on handoffs between marketing and data groups. A familiar sequence plays out:A marketer defines a campaign and requests an audience.A ticket moves to a data team for interpretation and SQL.The audience returns weeks later.The marketer realizes the audience lacks scale for ne...
Los Originales: 1. Además de ser el primer país en incluir el derecho al aborto, ahora las leyes de este país no conceden el divorcio solo por la falta de sexo en la pareja. 2. Conoce la manera adecuada de marcar el tarjetón en la consulta del 8 de marzo.
In a rare departure from our usual diet of online weirdos, this episode features an academic who is very much not a guru. We're joined by Julia Rohrer, a psychologist at Leipzig University whose work straddles the disciplinary boundaries of open science, research transparency, and causal inference. Julia is also an editor at Psychological Science and has spent much of the last decade politely pointing out that psychologists often don't quite know what they're estimating, why, or under which assumptions.We talk about the state of psychology after the replication crisis, whether open science reforms have genuinely improved research practice (or just added new boxes to tick), and why causal thinking is unavoidable even when researchers insist they are “only describing associations.” Julia explains why the standard dance of imply causality → deny causality → add boilerplate disclaimer is unhelpful, and argues instead for being explicit about the causal questions researchers actually care about and the assumptions required to answer them.Along the way we discuss images of scientists in the public and amongst the gurus, how post-treatment bias sneaks into even well-intentioned experimental designs, why specifying the estimand matters more than running ever-fancier models, and how psychology's current norms can potentially punish honesty about uncertainty. We also touch on her work on birth-order effects and offer some possible reasons for optimism.With all the guru talk, people sometimes ask us to recommend things that we like, and Julia's work is one such example!LinksJulia Rohrer's websiteThe 100% CI blogRohrer, J. M. (2024). Causal inference for psychologists who think that causal inference is not for them. Social and Personality Psychology Compass, 18(3), e12948.Rohrer, J. M., Tierney, W., Uhlmann, E. L., DeBruine, L. M., Heyman, T., Jones, B., ... & Yarkoni, T. (2021). Putting the self in self-correction: Findings from the loss-of-confidence project. Perspectives on Psychological Science, 16(6), 1255-1269.Rohrer, J. M., Egloff, B., & Schmukle, S. C. (2015). Examining the effects of birth order on personality. Proceedings of the National Academy of Sciences, 112(46), 14224-14229.BEMC MAY 2024 - Julia Rohrer - "Causal confusions correlate with casual conclusions"Dr. Tobias Dienlin - Less casual causal inference for experiments and longitudinal data: Research talk by Julia Rohrer
Gary Klein joins us to unpack the causal landscape of the 2025 World Series, exploring how decisions, context, and key moments shaped the final outcome.Gary Klein, Ph.D., authored Sources of Power: How People Make Decisions, and five other books plus three co-edited volumes. He is known for the cognitive models he described, such as the Recognition-Primed Decision (RPD) model, the Data/Frame model of sensemaking, the Management By Discovery model of planning in complex settings, and the Triple Path Model of Insight. He developed methods including the Pre-Mortem method of risk assessment, techniques for Cognitive Task Analysis, the ShadowBox training approach, and also helped Judith Orasau pioneer the Naturalistic Decision Making movement in 1989. Dr. Klein has decades of work experience in dozens of work domains, including military, healthcare, and emergency response.The company he started in 1978, Klein Associates, grew to 37 employees by the time he sold it in 2005. He kicked off his new company, ShadowBox LLC, in 2014. His hobbies include spending time with his grandchildren, watching movies, and beating John Schmitt at the Asian game of Go.You can find all of Gary's books, publications, and more at www.gary-klein.com________________________Where to find the hosts:Brian MoonBrian's websiteBrian's LinkedInBrian's TwitterLaura MilitelloLaura's websiteLaura's LinkedInLaura's Twitter
Wir besprechen in dieser Podcastfolge 5 typische Gründe, warum Gefühle verschwinden, welche Mechanismen dahinterstehen, welche Fehlannahmen viele Betroffene blockieren und welche Schritte helfen können, um emotional wieder erreichbar zu werden.Ebook "Gehen oder Bleiben"Date Night EbookMehr zu meinem Beratungsangebot (Paar-und Einzeltherapie)Kartenset für tiefe GesprächeStudienAron, A., Fisher, H., Mashek, D. J., Strong, G., & Brown, L. L. (2005). Reward, motivation, and emotion systems associated with early‐stage intense romantic love. Journal of Neurophysiology, 94(1), 327–337.Eisenberger, N. I., & Lieberman, M. D. (2004). Why rejection hurts: A common neural alarm system for physical and social pain. Trends in Cognitive Sciences, 8(7), 294–300.Gottman, J. M., & Levenson, R. W. (1999). What predicts change in marital interaction over time? Journal of Marriage and the Family, 61(4), 934–947.Lehmann, V., Denissen, J. J., Neumann, L., & van Zalk, M. H. (2016). Interpersonal behavior and relationship satisfaction. Journal of Social and Personal Relationships, 33(7), 1050–1071.McCullough, M. E., Root, L. M., & Cohen, A. D. (2010). Forgiveness and health. Social and Personality Psychology Compass, 3(6), 791–810.Mikulincer, M., & Shaver, P. R. (2016). Attachment in adulthood: Structure, dynamics, and change. Guilford Press.Peterson, C., & Seligman, M. E. (1984). Causal explanations as a risk factor for depression. Psychological Review, 91(3), 347–374.Reis, H. T., & Gable, S. L. (2015). Responsiveness. Current Opinion in Psychology, 1, 67–71.
You've built a toolkit over the last several episodes. Logical reasoning. Causal thinking. Mental models. Serious intellectual firepower. Now the uncomfortable question: When's the last time you actually used it to make a decision? Not a decision you think you made. One where you evaluated the options yourself. Weighed the evidence. Formed your own conclusion. […]
NOFire AI founder and CEO Spiros Economakis joins Dash0's Mirko Novakovic to discuss why traditional observability and post-incident RCA are no longer enough in an AI-accelerated engineering world. Drawing from real production failures and years of SRE leadership, Spiros explains how causal AI and agentic workflows can predict failures before code ever reaches production. The conversation also explores why the future of observability is moving from dashboards and alerts toward understanding, reasoning and proactive decision-making.
The hypergraphs generated by Wolfram Physics are complex and chaotic.The multiway graphs that trace every possible evolution of these hypergraphs become extremely complex and extremely chaotic after only a few iterations.The causal graphs that plot which of the events in these multiway graphs has to happen before which of the other events look like spaghetti.If we're going to find mass/energy or momentum in Wolfram Physics – or special relativity or general relativity or quantum mechanics – then the causal graph is the place to look.But if we're going to have to find all of physics in a causal graph that looks like spaghetti, then I give up.If we're going to find all of physics in the causal graph, then we're going to have to simplify.How to simplify the causal graph?Two ways:use a simpler rule; andcollapse multiple nodes representing the same event into a singlenode.Don't worry, by the time we're done, that'll all make sense!This is the pivotal episode in my exploration of Wolfram Physics. It establishes a firm foundation what comes next. From here on, we'll be able to make serious progress... towards mass/energy, momentum, special relativity, general relativity and quantum mechanics.—The Last Theory is hosted by Mark Jeffery founder of Open Web MindI release The Last Theory as a video too! Watch here.The full article is here.Kootenay Village Ventures Inc.
Can a digital replica of your heart save your life? In CXOTalk episode 902, Michael Krigsman talks with Dr. Joe Alexander, Director of the Medical and Health Informatics Lab at NTT Research, to explore the revolutionary world of Bio-Digital Twins.Discover how researchers are using mathematical modeling to build "computational replicas" of the human cardiovascular system. Dr. Alexander explains how these digital twins can predict heart failure, automate critical care in the ICU through closed-loop intervention systems, and pave the way for a future where personalized medicine is accessible to everyone.We dive deep into the science of treating the heart as an electrical circuit, the ethics of AI in medicine, and the "moonshot" goal of eliminating cardiovascular disease..
Ana Isabel Jiménez, alcaldesa de Alcalá de Guadaíra, en la inauguración de Pilatus: "La elección de Alcalá no es causal"
In this bitesize episode of the Safeguarding and Domestic Abuse Sector Podcast I talk about how a person's experience of trauma in childhood is included in reviews. Hear me talk about:
In this episode of Science of Reading: The Podcast, Susan Lambert is joined by University of Oregon College of Education Professor and Ann Swindells Chair in Education Gina Biancarosa, Ed.D., to explore how best to assess for comprehension. Gina elaborates on her extensive work developing more precise and informative measurements of reading comprehension and discusses think-aloud research, demonstrating how to infer for coherence, and examining how students who are struggling with comprehension tend to rely too heavily on making inferences or paraphrasing.Show notes:Submit your questions on comprehension!Access free, high-quality resources at our brand new, companion professional learning page. Connect with Gina on LinkedIn.Read “Diagnostic and Instructionally Relevant Measurement of Reading Comprehension”Resources:Listen to Season 2 of Amplify's Beyond My Years podcast.Join our community Facebook group.Connect with Susan Lambert.Quotes:"A lot of what we know about reading comprehension comes from think-alouds where you ask someone to tell you what they're thinking as they read." —Gina Biancarosa, Ed.D"To model reading comprehension, [try] thinking aloud in front of a classroom of students in a way that is instructive for them, and also authentic to the reading process." —Gina Biancarosa, Ed.D."Students are making causal inferences in their daily lives, when they watch movies, and when they're hearing stories. And so what we're really trying to do is get them to generalize these behaviors that they engage in outside of the task of reading, during reading." —Gina Biancarosa, Ed.D.Episode Timestamps:02:00 Introduction: Gina Biancarosa, Ed.D. and comprehension assessment08:00 How do we assess comprehension?14:00 Think-aloud research21:00 MOCCA (Multiple-Choice Online Causal Comprehension Assessment)24:00 Causal coherence30:00 Paraphrasers and elaborators33:00 Comprehension assessment research39:00 Professional development and comprehension assessment42:00 Closing thoughts*Timestamps are approximate, rounded to nearest minute
As a practical matter, how much effort do you put into pinning down the causes behind daily occurrences? To developmental psychologist Frank Keil, who studies causal thinking, that answer is likely along the lines of 'not enough.' A lack of causal thinking is both endemic, and, to an extent, hurtful these days, he argues, suggesting that lacking even simplified causal models makes things like the black box of artificial intelligence a potential problem. In this Social Science Bites podcast, Keil, the Charles C. and Dorathea S. Dilley Professor of Psychology and Linguistics at Yale University, outlines for interviewer David Edmonds how causal thinking is a skill we seem to have at an early age, but which diminishes as we grow up. "[K]ids, by the time they approach elementary school, are asking up to 200 'why' and 'how' questions a day," he explains. "Within a year or two up to starting school, they're down to two or three, often none." Furthermore, Keil sees this diminishment continuing in society today – and this comes as a cost. "I think it's making kids today be pushed more towards surface understanding, being user interface understanders. I think it makes influences more influential. To just say 'This is cool' as opposed to 'This is how it works.' One of the negative consequences is that we can get fooled by misinformation more; one of the best ways to debunk an expert is to ask them to explain the mechanism." At Yale, Keil directs the Cognition and Development lab. He has written several books, from academe-oriented books like Developmental Psychology: The Growth of Mind and Behavior, to more general reader titles like Wonder: Childhood and the Lifelong Love of Science. His awards include the Boyd R. McCandless Award from the American Psychological Association (Developmental Psychology), the Distinguished Scientific Award for an Early Career Contribution to Psychology from the American Psychological Association, a Guggenheim Fellowship, a fellowship at the Center for Advanced Study in the Behavioral Sciences at Stanford University, a MERIT Award from the National Institutes of Health, and the Ann L. Brown Award for Excellence in Developmental Research.
Dr Haidar Hobballah and Ali R. Khaki discuss the principle of sad al-dharāʾiʿ (blocking the means) and how Shīʿī legal thought approaches causal reasoning. They unpack the logic behind preventive rulings and explore their modern implications—from bioethics to environmental ethics—offering a rational framework for ethical decision-making in contemporary Islamic contexts.
Send us a textThe Causal Gap: Truly Responsible AI Needs to Understand the ConsequencesWhy do LLMs systematically drive themselves to extinction, and what does it have to do with evolution, moral reasoning, and causality?In this brand-new episode of Causal Bandits, we meet Zhijing Jin (Max Planck Institute for Intelligent Systems, University of Toronto) to answer these questions and look into the future of automated causal reasoning.In this episode, we discuss:- Zhijing's new work on the "causal scientist"- What's missing in responsible AI- Why ethics matter for agentic systems- Is causality a necessary element of moral reasoning?------------------------------------------------------------------------------------------------------Video version available on Youtube: https://youtu.be/Frb6eTW2ywkRecorded on Aug 18, 2025 in Tübingen, Germany.------------------------------------------------------------------------------------------------------About The GuestZhiijing Jin is a researcher scientist at Max Planck Institute for Intelligent Systems and an incoming Assistant Professor at the University of Toronto. Her work is focused on causality, natural language, and ethics, in particular in the context of large language models and multi-agent systems. Her work received multiple awards, including NeurIPS best paper award, and has been featured in CHIP Magazine, WIRED, and MIT News. She grew up in Shanghai. Currently she prepares to open her new research lab at the University of Toronto.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
$37 billion. That's how much gets wasted annually on marketing budgets because of poor attribution and misunderstanding of what actually drives results. Companies' credit campaigns that didn't work. They kill initiatives that were actually succeeding. They double down on coincidences while ignoring what's actually driving outcomes. Three executives lost their jobs this month for making […]
Federica Grosso from the Institute for Genetic and Biomedical Research (IRGB) of the National Research Council (CNR) in Monserrato, Italy, describes a #research paper she co-authored that was #published in Volume 17, Issue 8 of Aging-US, entitled “Causal relationships between gut microbiome and hundreds of age-related traits: evidence of a replicable effect on ApoM protein levels.” DOI - https://doi.org/10.18632/aging.206293 Corresponding author - Serena Sanna - serena.sanna@cnr.it Video interview - https://www.youtube.com/watch?v=qYg42_gn_pw Abstract In the past 20 years, the involvement of gut microbiome in human health has received particular attention, but its contribution to age-related diseases remains unclear. To address this, we performed a comprehensive two-sample Mendelian Randomization investigation, testing 55130 potential causal relationships between 37 traits representing gut microbiome composition and function and age-related phenotypes, including 1472 inflammatory and cardiometabolic circulating plasma proteins from UK Biobank Pharma Proteomic Project and 18 complex traits. A total of 91 causal relationships remained significant after multiple testing correction (false discovery rate p-value
In this episode of 'Loud and Clear,' host Francisco Cardenas discusses the complexities of programmatic marketing with Victoria Garcia Galarza, better known as VGG, from Causal. Together, they explore the importance of authentic brand voices, the perceptive capabilities of consumers, and the challenges of measuring business outcomes over media metrics. Victoria shares insights from her diverse cultural background and her journey in programmatic, from its early days to the current AI-driven landscape. They also discuss the role of AI in creative testing, the significance of transparency and trust with clients, and the evolving impact of retail media networks. The episode emphasizes continuous learning, adaptability, and prioritizing the consumer in digital marketing strategies.Guest: Victoria Garcia Galarza [VGG] Sales Director at CausalProducer: Victor Cornejo Tell Me More StudiosHost: Francisco Cardenas Principal of Digital Strategy & Integration at LERMA/Music: Rolf Ruiz
For over three decades, Felix Finster has been developing a unique and ambitious reformulation of physics known as Causal Fermion Systems (CFS). Physicists usually describe the world in terms of fields defined on a spacetime manifold. Within this familiar framework, abstract quantities such as correlations between matter fields at different points in spacetime can be computed. In mathematical language, these correlations are captured by operators acting on a Hilbert space. What Felix realized is that this process can be reversed. If you start with a suitable collection of operators on a Hilbert space, satisfying certain mathematical properties, you can in principle reconstruct the underlying spacetime and fields that would give rise to those operators as operators of correlations. In this sense, Causal Fermion Systems offers a dual description of reality. On the one hand, reality can be described in terms of symmetries, fields, and manifolds - the usual language of physics. On the other hand, CFS proposes that reality can just as well be described using abstract structures: Hilbert spaces, operators, and measures on sets of operators. Spacetime, matter, and everything we observe then emerges from these underlying mathematical quantities. The beauty of reformulating physics this way is that it opens up an entirely new framework in which to explore some of the deepest open questions in physics: What is spacetime like at the smallest scales? Why do we see precisely the particles we do in experiments? The hope is that within the CFS framework, answers to such questions might become more natural or even inevitable. Of course, we can't cover a 30-year research program in full detail in a single conversation. The goal here is to get a sense of the flavor of Felix's approach to physics. For the full details, you can explore Felix's books (e.g. https://www.cambridge.org/core/books/causal-fermion-systems/CCA6DE1E1F4DA3AC0EF6729664A5D5B9 ). ►Watch on YouTube: https://youtu.be/qQl51qifus0 ►Find out more about Felix's work here: https://www.uni-regensburg.de/mathematik/mathematik-1/startseite/index.html https://causal-fermion-system.com/
Causal Inquiry in International Relations (Oxford UP, 2024) by Adam R. C. Humphreys and Hidemi Suganami defends a new, philosophically informed account of the principles which must underpin any causal research in a discipline such as International Relations. Its central claim is that there is an underlying logic to all causal inquiry, at the core of which is the search for empirical evidence capable of ruling out competing accounts of how specific events were brought about. Although this crucial fact is obscured by the ‘culture of generalization' which predominates in contemporary social science, all causal knowledge ultimately depends on the provision of empirical support for concrete claims about specific events, located in space and time. Causal Inquiry in International Relations not only explores existing philosophical debates around causation; it also provides a detailed study of some of the most fundamental methodological questions which arise in the course of causal inquiry. Using examples drawn from philosophy and from the study of international relations, it demonstrates what is problematic about established ways of thinking, brings new clarity to both philosophical and methodological questions, and seeks to enhance collective understanding of the contribution that causal inquiry can make to empirically rich and critically aware scholarship about world politics. It concludes by situating ‘causal inquiry' in relation to other forms of inquiry employed in the study of world politics, emphasizing especially the often unnoticed dependence of causal inquiry on precisely the kind of knowledge of specific events which historians are well placed to provide. Adam Humphreys is Associate Professor and Head of Department in the Department of Politics and International Relations, University of Reading. He joined the University of Reading in 2013, having previously been a British Academy Post-Doctoral Fellow at the University of Oxford (2007-10) and Fellow in Politics at Brasenose College, Oxford (2010-13). His principal research interests are in International Relations theory and meta-theory, especially causation and causal explanation, realism and neo-realism, the English School, and the relationship between theory and history. He also has research interests in British foreign and defence policy, strategy, and the ethics of war.Hidemi Suganami studied International Relations at Tokyo, Aberystwyth, and London Universities. His first academic appointment was at Keele in 1975, where he later became Professor of the Philosophy of International Relations. In 2004, he moved to Aberystwyth, where currently he is Emeritus Professor of International Politics. His publications include: The Domestic Analogy and World Order Proposals (1989); On the Causes of War (1996); and, with Andrew Linklater, The English School of International Relations (2006). Over a number of years, he has been studying philosophical issues surrounding causation and explanation in International Relations. Stephen Satkiewicz is an independent scholar with research areas spanning Civilizational Sciences, Social Complexity, Big History, Historical Sociology, Military History, War Studies, International Relations, Geopolitics, and 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
Causal Inquiry in International Relations (Oxford UP, 2024) by Adam R. C. Humphreys and Hidemi Suganami defends a new, philosophically informed account of the principles which must underpin any causal research in a discipline such as International Relations. Its central claim is that there is an underlying logic to all causal inquiry, at the core of which is the search for empirical evidence capable of ruling out competing accounts of how specific events were brought about. Although this crucial fact is obscured by the ‘culture of generalization' which predominates in contemporary social science, all causal knowledge ultimately depends on the provision of empirical support for concrete claims about specific events, located in space and time. Causal Inquiry in International Relations not only explores existing philosophical debates around causation; it also provides a detailed study of some of the most fundamental methodological questions which arise in the course of causal inquiry. Using examples drawn from philosophy and from the study of international relations, it demonstrates what is problematic about established ways of thinking, brings new clarity to both philosophical and methodological questions, and seeks to enhance collective understanding of the contribution that causal inquiry can make to empirically rich and critically aware scholarship about world politics. It concludes by situating ‘causal inquiry' in relation to other forms of inquiry employed in the study of world politics, emphasizing especially the often unnoticed dependence of causal inquiry on precisely the kind of knowledge of specific events which historians are well placed to provide. Adam Humphreys is Associate Professor and Head of Department in the Department of Politics and International Relations, University of Reading. He joined the University of Reading in 2013, having previously been a British Academy Post-Doctoral Fellow at the University of Oxford (2007-10) and Fellow in Politics at Brasenose College, Oxford (2010-13). His principal research interests are in International Relations theory and meta-theory, especially causation and causal explanation, realism and neo-realism, the English School, and the relationship between theory and history. He also has research interests in British foreign and defence policy, strategy, and the ethics of war.Hidemi Suganami studied International Relations at Tokyo, Aberystwyth, and London Universities. His first academic appointment was at Keele in 1975, where he later became Professor of the Philosophy of International Relations. In 2004, he moved to Aberystwyth, where currently he is Emeritus Professor of International Politics. His publications include: The Domestic Analogy and World Order Proposals (1989); On the Causes of War (1996); and, with Andrew Linklater, The English School of International Relations (2006). Over a number of years, he has been studying philosophical issues surrounding causation and explanation in International Relations. Stephen Satkiewicz is an independent scholar with research areas spanning Civilizational Sciences, Social Complexity, Big History, Historical Sociology, Military History, War Studies, International Relations, Geopolitics, and 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
Causal Inquiry in International Relations (Oxford UP, 2024) by Adam R. C. Humphreys and Hidemi Suganami defends a new, philosophically informed account of the principles which must underpin any causal research in a discipline such as International Relations. Its central claim is that there is an underlying logic to all causal inquiry, at the core of which is the search for empirical evidence capable of ruling out competing accounts of how specific events were brought about. Although this crucial fact is obscured by the ‘culture of generalization' which predominates in contemporary social science, all causal knowledge ultimately depends on the provision of empirical support for concrete claims about specific events, located in space and time. Causal Inquiry in International Relations not only explores existing philosophical debates around causation; it also provides a detailed study of some of the most fundamental methodological questions which arise in the course of causal inquiry. Using examples drawn from philosophy and from the study of international relations, it demonstrates what is problematic about established ways of thinking, brings new clarity to both philosophical and methodological questions, and seeks to enhance collective understanding of the contribution that causal inquiry can make to empirically rich and critically aware scholarship about world politics. It concludes by situating ‘causal inquiry' in relation to other forms of inquiry employed in the study of world politics, emphasizing especially the often unnoticed dependence of causal inquiry on precisely the kind of knowledge of specific events which historians are well placed to provide. Adam Humphreys is Associate Professor and Head of Department in the Department of Politics and International Relations, University of Reading. He joined the University of Reading in 2013, having previously been a British Academy Post-Doctoral Fellow at the University of Oxford (2007-10) and Fellow in Politics at Brasenose College, Oxford (2010-13). His principal research interests are in International Relations theory and meta-theory, especially causation and causal explanation, realism and neo-realism, the English School, and the relationship between theory and history. He also has research interests in British foreign and defence policy, strategy, and the ethics of war.Hidemi Suganami studied International Relations at Tokyo, Aberystwyth, and London Universities. His first academic appointment was at Keele in 1975, where he later became Professor of the Philosophy of International Relations. In 2004, he moved to Aberystwyth, where currently he is Emeritus Professor of International Politics. His publications include: The Domestic Analogy and World Order Proposals (1989); On the Causes of War (1996); and, with Andrew Linklater, The English School of International Relations (2006). Over a number of years, he has been studying philosophical issues surrounding causation and explanation in International Relations. Stephen Satkiewicz is an independent scholar with research areas spanning Civilizational Sciences, Social Complexity, Big History, Historical Sociology, Military History, War Studies, International Relations, Geopolitics, and 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
Causal Inquiry in International Relations (Oxford UP, 2024) by Adam R. C. Humphreys and Hidemi Suganami defends a new, philosophically informed account of the principles which must underpin any causal research in a discipline such as International Relations. Its central claim is that there is an underlying logic to all causal inquiry, at the core of which is the search for empirical evidence capable of ruling out competing accounts of how specific events were brought about. Although this crucial fact is obscured by the ‘culture of generalization' which predominates in contemporary social science, all causal knowledge ultimately depends on the provision of empirical support for concrete claims about specific events, located in space and time. Causal Inquiry in International Relations not only explores existing philosophical debates around causation; it also provides a detailed study of some of the most fundamental methodological questions which arise in the course of causal inquiry. Using examples drawn from philosophy and from the study of international relations, it demonstrates what is problematic about established ways of thinking, brings new clarity to both philosophical and methodological questions, and seeks to enhance collective understanding of the contribution that causal inquiry can make to empirically rich and critically aware scholarship about world politics. It concludes by situating ‘causal inquiry' in relation to other forms of inquiry employed in the study of world politics, emphasizing especially the often unnoticed dependence of causal inquiry on precisely the kind of knowledge of specific events which historians are well placed to provide. Adam Humphreys is Associate Professor and Head of Department in the Department of Politics and International Relations, University of Reading. He joined the University of Reading in 2013, having previously been a British Academy Post-Doctoral Fellow at the University of Oxford (2007-10) and Fellow in Politics at Brasenose College, Oxford (2010-13). His principal research interests are in International Relations theory and meta-theory, especially causation and causal explanation, realism and neo-realism, the English School, and the relationship between theory and history. He also has research interests in British foreign and defence policy, strategy, and the ethics of war.Hidemi Suganami studied International Relations at Tokyo, Aberystwyth, and London Universities. His first academic appointment was at Keele in 1975, where he later became Professor of the Philosophy of International Relations. In 2004, he moved to Aberystwyth, where currently he is Emeritus Professor of International Politics. His publications include: The Domestic Analogy and World Order Proposals (1989); On the Causes of War (1996); and, with Andrew Linklater, The English School of International Relations (2006). Over a number of years, he has been studying philosophical issues surrounding causation and explanation in International Relations. Stephen Satkiewicz is an independent scholar with research areas spanning Civilizational Sciences, Social Complexity, Big History, Historical Sociology, Military History, War Studies, International Relations, Geopolitics, and Russian and East European history. Learn more about your ad choices. Visit megaphone.fm/adchoices
Proudly sponsored by PyMC Labs, the Bayesian Consultancy. Book a call, or get in touch!Get early access to Alex's next live-cohort courses!Enroll in the Causal AI workshop, to learn live with Alex (15% off if you're a Patron of the show)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 inference is crucial for understanding the impact of interventions in various fields.ChiRho is a causal probabilistic programming language that bridges mechanistic and data-driven models.ChiRho allows for easy manipulation of causal models and counterfactual reasoning.The design of ChiRho emphasizes modularity and extensibility for diverse applications.Causal inference requires careful consideration of assumptions and model structures.Real-world applications of causal inference can lead to significant insights in science and engineering.Collaboration and communication are key in translating causal questions into actionable models.The future of causal inference lies in integrating probabilistic programming with scientific discovery.Chapters:05:53 Bridging Mechanistic and Data-Driven Models09:13 Understanding Causal Probabilistic Programming12:10 ChiRho and Its Design Principles15:03 ChiRho's Functionality and Use Cases17:55 Counterfactual Worlds and Mediation Analysis20:47 Efficient Estimation in ChiRho24:08 Future Directions for Causal AI50:21 Understanding the Do-Operator in Causal Inference56:45 ChiRho's Role in Causal Inference and Bayesian Modeling01:01:36 Roadmap and Future Developments for ChiRho01:05:29 Real-World Applications of Causal Probabilistic Programming01:10:51 Challenges in Causal Inference Adoption01:11:50 The Importance of Causal Claims in Research01:18:11 Bayesian Approaches to Causal Inference01:22:08 Combining Gaussian Processes with Causal Inference01:28:27 Future Directions in Probabilistic Programming and Causal InferenceThank 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...
Why do we procrastinate, overthink, avoid, or hold back just when things matter most? Your social media feed at 2 am calls this self-sabotage, but is it?In this episode of the Scenic Route, we unpack fresh psychology research that flips the story of self-sabotage on its head. You'll discover three hidden patterns that shape how we repeat harmful choices:Sensitives: quick learners who adapt after mistakesUnawares: people who miss the cause-and-effect until it's explainedCompulsives: those who know better but can't break the cycleYou'll hear why common advice like “just use willpower” or “learn from your mistakes” fails so many of us, and what actually works instead.Because here's the twist: you're not broken, lazy, or your own worst enemy. What looks like sabotage is often safety in disguise. And once you see that, everything changes.Paper mentioned:Zeng, L., Park, H. R. P., McNally, G. P., Jean-Richard-dit-Bressel, P., et al. (2025). Causal inference and cognitive-behavioral integration deficits drive stable variation in human punishment sensitivity. Communications Psychology, 3, Article 103._____________________________________________________________________ Visit jenniferwalter.me – your cosy corner where recovering perfectionists, misfits, and those done pretending to be fine find space to breathe, dream, and create real change."
Patrick McKenzie (patio11) is joined by Emmett Shear, co-founder of Twitch, former interim CEO of OpenAI, who now runs Softmax AI alignment. Emmett argues that current AI safety approaches focused on "systems of control" are fundamentally flawed and proposes "organic alignment" instead—where AI systems develop genuine care for their local communities rather than following rigid rules. –Full transcript available here: www.complexsystemspodcast.com/ai-alignment-with-emmett-shear/–Sponsor: MercuryThis episode is brought to you by Mercury, the fintech trusted by 200K+ companies — from first milestones to running complex systems. Mercury offers banking that truly understands startups and scales with them. Start today at Mercury.com Mercury is a financial technology company, not a bank. Banking services provided by Choice Financial Group, Column N.A., and Evolve Bank & Trust; Members FDIC.–Links:Softmax - https://www.softmax.com/–Timestamps:(01:26) Understanding AI alignment(04:42) The concept of universal constructors(13:45) AI's rapid progress and practical applications(19:08) Sponsor: Mercury(20:19) AI's impact on work(34:59) AI's sensory and action space(42:10) User intent vs. user request(44:35) The illusion of a perfect AI(49:57) Causal emergence and system dynamics(55:19) Reflective and intentional alignment(01:01:08) Engineering challenges in AI alignment(01:04:15) The future of AI(01:26:40) Wrap
BUFFALO, NY — September 9, 2025 — A new #research paper was #published in Volume 17, Issue 8 of Aging-US on August 1, 2025, titled “Causal relationships between gut microbiome and hundreds of age-related traits: evidence of a replicable effect on ApoM protein levels.” In this study, Federica Grosso, Daniela Zanetti, and Serena Sanna from the Institute for Genetic and Biomedical Research (IRGB) of the National Research Council (CNR), Italy, uncovered new associations between gut microbiome and the aging process. The researchers found that certain microbial characteristics may causally influence proteins in the blood linked to inflammation and heart health. These findings could help explain how age-related diseases like cardiovascular conditions and macular degeneration are influenced by changes in the gut ecosystem. The gut microbiome, the collection of microorganisms living in the digestive system, plays a major role in immune function and metabolic health. As people age, this microbial community shifts, often leading to imbalances associated with inflammation and chronic disease. To explore how these changes might affect the body, the researchers used Mendelian Randomization—a method that leverages genetic data—to test over 55,000 possible causal connections between gut microbial characteristics and age-related health indicators. The study identified 91 significant causal relationships. Among them, the researchers found that higher levels of certain gut bacteria were associated with increased risk of age-related macular degeneration. Another finding was the association between a metabolic pathway in the gut, called “purine nucleotides degradation II,” and lower levels of apolipoprotein M (ApoM), a protein that helps protect against heart disease. This result was validated using data from an independent study, strengthening the evidence. “Unlike previous studies, we performed replication analyses for the significant results using independent GWAS datasets, a fundamental step that has often been overlooked.” The study also revealed how some bacteria may affect protein levels differently depending on a person's blood type. Specifically, in individuals with blood type A, certain gut microbes that can break down a sugar called GalNAc may influence proteins related to inflammation and cardiovascular health. This suggests that personalized approaches to managing age-related diseases could consider both gut microbiota and genetic factors like blood type. The research team followed strict guidelines to reduce false findings and confirmed its key results in independent datasets. By carefully testing for reverse causality and other biases, the authors provided strong evidence that the gut microbiome can influence critical aspects of aging biology. Although more research is needed to fully understand the biological pathways involved, these findings suggest that targeting the gut microbiota might help delay or reduce age-related inflammation and disease. The study lays a foundation for future therapeutic strategies that could include diet, probiotics, or other microbiome-based interventions. DOI - https://doi.org/10.18632/aging.206293 Corresponding author - Serena Sanna - serena.sanna@cnr.it Abstract video - https://www.youtube.com/watch?v=CWky6jlHKUs Subscribe for free publication alerts from Aging - https://www.aging-us.com/subscribe-to-toc-alerts To learn more about the journal, please visit our website at https://www.Aging-US.com and connect with us on social media at: Facebook - https://www.facebook.com/AgingUS/ X - https://twitter.com/AgingJrnl Instagram - https://www.instagram.com/agingjrnl/ YouTube - https://www.youtube.com/@AgingJournal LinkedIn - https://www.linkedin.com/company/aging/ Bluesky - https://bsky.app/profile/aging-us.bsky.social Pinterest - https://www.pinterest.com/AgingUS/ Spotify - https://open.spotify.com/show/1X4HQQgegjReaf6Mozn6Mc MEDIA@IMPACTJOURNALS.COM
CP-3010 is the anomalous byproduct of a nearly undetectable entity, hereby classified as SCP-3010-1. SCP-3010 is characterized as a sensation of"being watched", similar to that of being intensely stared at or observed unwillingly by another human or sentient being.Content Warnings: Unreality, nyctophobia, scopophobia.TranscriptPatrons May 2-10Joey,, Sebastian Rose, BlazoticPG, Ellie McCartney, James Good, Juice Man, Te Puhi Nathan, Jacob Byers, Henning, Fernando Tellez, Sienna Athy, Aaron Irvin, Stacy Krugger, Pyrelight, Zachary Hutchins, Jae Jump, and Diego Rivera!Cast & Crew SCP Archives was created by Pacific S. Obadiah & Jon GrilzSCP-3010 was written by iznarothScript by Daisy McNamaraComputer - Nichole GoodnightCap - Janine BowerMTF-066-7 - Jonathan McDonaldMTF-066-1 - Dexter HowardMTF-066-3 - Rebekah McLoughlinMTF-066-5 - Kit PatersonD-17729 - Russ MoreObrendt - Stephen IndrisanoSCP-3010 - Kayla TemshivArt - Eduardo Valdés-HeviaTheme Song - Mattie Roi BergerOriginal Music - Newton SchottelkotteDialogue Editor - Nate DuFortSound Designer - Brad ColbroockShowrunner - Daisy McNamaraCreative Director - Pacific S. ObadiahExecutive Producer - Tom Owen Presented by Bloody FMwww.Bloody-Disgusting.comwww.SCParchives.com Patreon: https://www.patreon.com/scp_podStore: https://store.dftba.com/collections/scp-archivesInstagram: https://www.instagram.com/scp_pod/Bluesky: https://bsky.app/profile/scparchives.bsky.socialDiscord: https://discord.gg/tJEeNUzeZXTikTok: https://www.tiktok.com/@scppodYouTube: https://www.youtube.com/c/scparchives
Commentary by Dr. Naoto Otsuka.
Commentary by Dr. Jian'an Wang.
Happy 801 Day, Salt Lake! Host Ali Vallarta, executive producer Emily Means, and Salt Lake Tribune reporter Andy Larsen count down their favorite things about our city. Plus, Heber residents push back against a new church, a cute new avalanche pup, and gourmet fast casual. Resources and references: Residents to appeal Heber Valley temple decision to Utah Supreme Court [KPCW] Updated guidance on banned books in Utah schools: you own it, you can bring it [KUER] Join us for 801 Day at the Gallivan Center on Friday, Aug. 1. RSVP here! Become a member of City Cast Salt Lake today! It's the best way to support our work and help make sure we are around for years to come. Get all the details and sign up at membership.citycast.fm. Subscribe to Hey Salt Lake, our daily morning newsletter. You can also find us on Instagram @CityCastSLC. Looking to advertise on City Cast Salt Lake? Check out our options for podcast and newsletter ads. Learn more about the sponsors of this episode: Tracy Aviary Workshopslc.com - use code CITYCAST for 20% off. Live Crude - Get $10 off your first CRUDE purchase with promo code CITYCASTSLC Learn more about your ad choices. Visit megaphone.fm/adchoices
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.
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.
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
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
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...
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
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