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.
Season 1: The Book of Why
In Season 1, two AI-generated voices guide us through the foundational concepts of causality, exploring groundbreaking ideas from The Book of Why.
Episode 1: From Data to Discovery - How We Learned to Ask 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.
Episode 2: The Causality Quest - From Superstition to Science
For 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.
Episode 3: The Detective’s Dilemma - How We Infer Causes from Evidence
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.
Episode 4: Untangling the Web – How to Overcome Hidden Bias in Data
Not all correlations tell the truth. Hidden biases and confounding factors can distort our understanding of cause and effect, leading to flawed conclusions in science, medicine, and beyond. In this episode, we explore how controlled experiments, causal diagrams, and statistical techniques help separate real causation from misleading associations. From biblical experiments to modern clinical trials, we uncover the tools that allow us to “deconfound” our reasoning and truly see cause and effect.
Episode 5: Breaking the Illusion - How to Prove Causation Without Experiments
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?
Episode 6: The Paradox Problem – When Data Misleads Our Intuition
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?
Episode 7: Counterfactual Worlds – Imagining the What-Ifs of Causality
What if things had happened differently? Counterfactual reasoning, the ability to imagine alternate realities, lies at the heart of human intelligence and scientific discovery. In this episode, we explore how counterfactuals allow us to answer questions like “What would have happened if I had chosen differently?” or “Did this treatment actually save lives?” From legal judgments to AI decision-making, we examine how this powerful tool reshapes our understanding of causality and prediction. How do counterfactuals help us uncover true cause-and-effect relationships? And why do even the most advanced AI systems struggle with them?
Episode 8: Imagining the Impossible - How Counterfactuals Shape Our World
What if Cleopatra’s nose had been shorter? What if Joe had taken the aspirin? In this episode, we ascend to the top rung of the Ladder of Causation to explore counterfactuals—alternate realities that reveal what is and what could have been. From assigning blame to making predictions, counterfactual reasoning shapes scientific discovery, legal judgments, and everyday decisions. We delve into two key approaches: structural causal models, offering a precise computational framework, and the potential outcomes model, rooted in statistics. Which approach is more effective, and how can machines learn to think in “what ifs” like humans?
Episode 9: Tracing the Invisible - How Causes Travel Through the World
This episode explores the concept of mediation, the often-hidden processes that link causes to their effects. Using historical and scientific examples, from James Lind’s discovery about scurvy to modern intelligence research, it highlights how understanding the chain of events between an action and its outcome is essential to uncovering the true mechanisms behind observed effects. The episode also honors Barbara Burks, a trailblazing scientist who used path diagrams to untangle nature and nurture long before her time. Ultimately, it asks why some causes act directly while others operate through mediators, and how grasping these dynamics can reshape fields like medicine, scientific policy, and
Episode 10: Can AI Ask Why? The Final Frontier of Causal Reasoning
In this season finale, we explore the fascinating intersection of artificial intelligence and causal reasoning. While modern AI systems excel at finding patterns in data, they often miss what comes naturally to humans: understanding why things happen. From the foundations of machine learning to the latest breakthroughs in causal AI, we examine the gap between correlation-based prediction and true causal understanding. Can we teach machines to think counterfactually? Will AI ever truly grasp causation? Drawing on insights from computer science, cognitive psychology, and philosophy, we explore what it means for machines to not just predict the future, but to understand it. The episode concludes by looking ahead to the transformative potential of causal AI in fields ranging from medicine to scientific discovery.