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.
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