Predictive models are widely used to identify high-risk patients and populations.
They promise earlier intervention, better resource allocation, and improved outcomes.

But what if prediction alone is not enough to actually change what happens next?

In this episode, we break down the critical difference between prediction and causation—and why models that perform well statistically can still fail when used in real-world decision-making. You will learn why predicting risk is not the same as knowing what action to take, and how this gap affects healthcare and public health systems.

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