Decoding Causality

Amir Rafe
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Welcome to Decoding Causality, where conversations unravel the mysteries of cause and effect. Inspired by the ideas explored in The Book of Why, this podcast delves into the fascinating world of causal reasoning, counterfactuals, and the science of asking ‘why.’ Each episode breaks down complex concepts into accessible and thought-provoking insights. Perfect for researchers, students, and curious minds, this podcast offers a fresh take on decision-making and discovery.

集數

  1. S1E10. Can AI Ask Why? The Final Frontier of Causal Reasoning

    2025/04/21

    S1E10. Can AI Ask Why? The Final Frontier of Causal Reasoning

    Season 1: The Book of Why As Season 1 comes to a close, we explore the convergence of big data, artificial intelligence, and the age-old question of “why.” While machines have become astonishingly good at pattern recognition, they still struggle with the essence of human understanding: causal reasoning. In this episode, we reflect on how the Causal Revolution challenged traditional statistics and ask whether AI can ever truly emulate human curiosity and imagination. What does it take for machines to not only predict outcomes but explain them? Can we teach AI to distinguish correlation from causation—or even reason about counterfactuals? Join us for a thought-provoking finale as we examine the future of causal thinking in a world increasingly driven by data and algorithms. 🔍 Stay Connected 📧 Email: ⁠⁠⁠⁠amir.rafe@usu.edu⁠⁠⁠⁠ 🌐 Website: ⁠⁠⁠⁠https://pozapas.github.io/⁠⁠⁠⁠ 🔗 LinkedIn: ⁠⁠⁠⁠https://www.linkedin.com/in/amir-rafe-08770854/⁠⁠⁠⁠ 🐦 X: ⁠⁠⁠⁠https://x.com/rafeamir⁠⁠

    14 分鐘
  2. S1E09. Tracing the Invisible: How Causes Travel Through the World

    2025/04/08

    S1E09. Tracing the Invisible: How Causes Travel Through the World

    Season 1: The Book of Why What lies between cause and effect? In this episode, we delve into the concept of mediation, the hidden pathways that connect actions to outcomes. From James Lind’s battle with scurvy to groundbreaking diagrams in intelligence research, we explore how scientists uncover the mechanisms that explain how and why effects occur. When we ask, “Does Drug B prevent heart attacks?”, we’re really asking: “Through what chain of events?” Understanding these chains can mean the difference between truth and tragic error. We also reflect on the legacy of Barbara Burks—a pioneering woman in science who challenged the norms of her time by visualizing the complex threads of nature and nurture through path diagrams, decades ahead of her field. Why do some causes work directly while others weave through mediators? And how can this knowledge transform scientific policy, medicine, and artificial intelligence? Join us as we climb deeper into the Ladder of Causation and learn to trace the steps between action and consequence. 🔍 Stay Connected 📧 Email: ⁠⁠⁠amir.rafe@usu.edu⁠⁠⁠ 🌐 Website: ⁠⁠⁠https://pozapas.github.io/⁠⁠⁠ 🔗 LinkedIn: ⁠⁠⁠https://www.linkedin.com/in/amir-rafe-08770854/⁠⁠⁠ 🐦 X: ⁠⁠⁠https://x.com/rafeamir⁠⁠

    28 分鐘
  3. S1E08. Imagining the Impossible: How Counterfactuals Shape Our World

    2025/03/25

    S1E08. Imagining the Impossible: How Counterfactuals Shape Our World

    Season 1: The Book of Why What if Cleopatra’s nose had been shorter? What if Joe had taken the aspirin? In this episode, we climb to the top rung of the Ladder of Causation and explore the fascinating world of counterfactuals, alternate realities that help us understand what is and what could have been. We’ll examine how imagining different scenarios is more than philosophical musing, it's central to assigning blame, making predictions, and defining responsibility. From climate change attribution to legal causation, we uncover how counterfactual reasoning powers everything from scientific discovery to everyday decision-making. We also compare two powerful approaches to counterfactuals: the structural causal models that give us a precise computational framework, and the potential outcomes model rooted in statistics. What makes one more effective than the other? And how can machines learn to think in “what ifs” the way humans do? 🔍 Stay Connected 📧 Email: ⁠⁠amir.rafe@usu.edu⁠⁠ 🌐 Website: ⁠⁠https://pozapas.github.io/⁠⁠ 🔗 LinkedIn: ⁠⁠https://www.linkedin.com/in/amir-rafe-08770854/⁠⁠ 🐦 X: ⁠⁠https://x.com/rafeamir⁠⁠

    16 分鐘
  4. S1E07. Climbing the Causal Mountain: How to Predict the Impact of Interventions

    2025/03/03

    S1E07. Climbing the Causal Mountain: How to Predict the Impact of Interventions

    Season 1: The Book of Why Understanding what happens when we take action—rather than just observe—is at the heart of causal reasoning. In this episode, we ascend to the second rung of the Ladder of Causation: intervention. How can we predict the effects of a new drug, a change in policy, or even a personal decision if we’ve never observed it before? We explore the tools that make this possible: back-door and front-door adjustments, instrumental variables, and the powerful do-calculus. Why are randomized controlled trials celebrated as the “gold standard”? When can observational data be just as good—or even better? And what happens when we don’t have access to all the confounders? From elegant theory to practical tools, we reveal how modern causal diagrams and equations let us do the unthinkable: predict the consequences of actions not yet taken. 🔍 Stay Connected 📧 Email: ⁠⁠amir.rafe@usu.edu⁠⁠ 🌐 Website: ⁠⁠https://pozapas.github.io/⁠⁠ 🔗 LinkedIn: ⁠⁠https://www.linkedin.com/in/amir-rafe-08770854/⁠⁠ 🐦 X: ⁠⁠https://x.com/rafeamir⁠⁠

    24 分鐘
  5. S1E06. The Paradox Problem: When Data Misleads Our Intuition

    2025/03/03

    S1E06. The Paradox Problem: When Data Misleads Our Intuition

    Season 1: The Book of Why Sometimes, the numbers lie—or at least, they seem to. In this episode, we dive into some of the most famous paradoxes in statistics and probability, from Simpson’s paradox to the Monty Hall problem. These puzzles reveal the hidden tensions between correlation and causation, challenging our intuition and exposing the limitations of traditional data analysis. Why do paradoxes arise? How can they mislead us in decision-making, science, and AI? And what does it take to resolve them? Join us as we unravel the paradoxes that have baffled researchers for decades and explore how causal reasoning provides the key to seeing through the illusion. 🔍 Stay Connected 📧 Email: ⁠⁠amir.rafe@usu.edu⁠⁠ 🌐 Website: ⁠⁠https://pozapas.github.io/⁠⁠ 🔗 LinkedIn: ⁠⁠https://www.linkedin.com/in/amir-rafe-08770854/⁠⁠ 🐦 X: ⁠⁠https://x.com/rafeamir⁠⁠

    12 分鐘
  6. S1E05. Breaking the Illusion: How to Prove Causation Without Experiments

    2025/02/27

    S1E05. Breaking the Illusion: How to Prove Causation Without Experiments

    Season 1: The Book of Why How can we prove that one thing causes another when experiments are impossible or unethical? In this episode, we explore the challenge of establishing causation using observational data and statistical reasoning. From historical scientific debates to modern breakthroughs, we examine how researchers separate real causal relationships from misleading correlations. What methods allow us to move beyond association? How can causal inference reshape the way we approach science, medicine, and AI? Join us as we uncover the principles that help us break the illusion of mere correlation and reveal true cause and effect. 🔍 Stay Connected 📧 Email: ⁠⁠amir.rafe@usu.edu⁠⁠ 🌐 Website: ⁠⁠https://pozapas.github.io/⁠⁠ 🔗 LinkedIn: ⁠⁠https://www.linkedin.com/in/amir-rafe-08770854/⁠⁠ 🐦 X: ⁠⁠https://x.com/rafeamir⁠⁠

    16 分鐘
  7. S1E04. Untangling the Web: How to Overcome Hidden Bias in Data

    2025/02/26

    S1E04. Untangling the Web: How to Overcome Hidden Bias in Data

    Season 1: The Book of Why Not all correlations are what they seem. Hidden biases, lurking variables, and confounding factors can distort our understanding of cause and effect—leading to flawed conclusions in science, medicine, and everyday decision-making. In this episode, we uncover the challenge of confounding and how controlled experiments, causal diagrams, and statistical techniques help us separate real causation from misleading associations. From biblical experiments to modern-day clinical trials, we explore the evolution of methods designed to "deconfound" our reasoning. How do we avoid false conclusions? Can we make valid causal claims from observational data? And what does this mean for AI systems trying to make sense of the world? Join us as we tackle one of the biggest hurdles in causal inference and reveal how we can truly "see" cause and effect. 🔍 Stay Connected 📧 Email: ⁠⁠amir.rafe@usu.edu⁠⁠ 🌐 Website: ⁠⁠https://pozapas.github.io/⁠⁠ 🔗 LinkedIn: ⁠⁠https://www.linkedin.com/in/amir-rafe-08770854/⁠⁠ 🐦 X: ⁠⁠https://x.com/rafeamir⁠⁠

    14 分鐘
  8. S1E03. The Detective’s Dilemma: How We Infer Causes from Evidence

    2025/02/22

    S1E03. The Detective’s Dilemma: How We Infer Causes from Evidence

    Season 1: The Book of Why What do Sherlock Holmes and artificial intelligence have in common? Both rely on evidence to reach conclusions, but only one truly understands why things happen. In this episode, we dive into the logic of inference, exploring how the Reverend Thomas Bayes and the principles of probability laid the groundwork for reasoning from effect to cause. From medical diagnoses to forensic science, we examine how Bayesian networks help us navigate uncertainty—and why causal inference remains the missing piece in today’s AI revolution. 🔍 Stay Connected 📧 Email: ⁠⁠amir.rafe@usu.edu⁠⁠ 🌐 Website: ⁠⁠https://pozapas.github.io/⁠⁠ 🔗 LinkedIn: ⁠⁠https://www.linkedin.com/in/amir-rafe-08770854/⁠⁠ 🐦 X: ⁠⁠https://x.com/rafeamir⁠⁠

    20 分鐘
  9. S1E02. The Causality Quest: From Superstition to Science

    2025/02/21

    S1E02. The Causality Quest: From Superstition to Science

    Season 1: The Book of WhyFor centuries, humanity relied on intuition, folklore, and trial and error to understand the world. But how did we transition from anecdotal reasoning to the rigorous science of causal inference? In this episode, we uncover the historical breakthroughs that laid the foundation for modern causality, from the early insights of Francis Galton to the statistical revolution that shaped how we analyze cause and effect. What lessons can we learn from the past, and how do they impact the way we approach data and decision-making today? 🔍 Stay Connected 📧 Email: ⁠amir.rafe@usu.edu⁠ 🌐 Website: ⁠https://pozapas.github.io/⁠ 🔗 LinkedIn: ⁠https://www.linkedin.com/in/amir-rafe-08770854/⁠ 🐦 X: ⁠https://x.com/rafeamir⁠

    18 分鐘
  10. S1E01. From Data to Discovery: How We Learned to Ask Why

    2025/02/21

    S1E01. From Data to Discovery: How We Learned to Ask Why

    Season 1: The Book of Why What separates humans from other species, and from today’s AI systems, is not just the ability to observe patterns but the power to ask why. In this episode, we explore the profound shift that enabled humanity to move beyond mere data collection to uncovering the hidden web of cause and effect. From the Garden of Eden to the Cognitive Revolution, we trace the origins of causal reasoning and how it transformed our ability to understand and shape the world. Why do explanations matter more than raw facts? And what does this mean for the future of artificial intelligence? Join us as we take the first step up the Ladder of Causation. 🔍 Stay Connected 📧 Email: amir.rafe@usu.edu🌐 Website: https://pozapas.github.io/🔗 LinkedIn: https://www.linkedin.com/in/amir-rafe-08770854/🐦 X: https://x.com/rafeamir

    15 分鐘
  • 共 10 集

簡介

Welcome to Decoding Causality, where conversations unravel the mysteries of cause and effect. Inspired by the ideas explored in The Book of Why, this podcast delves into the fascinating world of causal reasoning, counterfactuals, and the science of asking ‘why.’ Each episode breaks down complex concepts into accessible and thought-provoking insights. Perfect for researchers, students, and curious minds, this podcast offers a fresh take on decision-making and discovery.

資訊

  • 創作者
    Amir Rafe
  • 活躍年代
    2025年
  • 集數
    10
  • 年齡分級
    兒少適宜
  • 版權
    © Amir Rafe
  • 節目網站
    Decoding Causality