How Does Causal Effects via DAGs | How to Handle Unobserved Confounders Work?

Causal effects via DAGs help us see how things cause each other, even when we can’t see everything that’s going on.

Imagine you're trying to figure out if eating ice cream makes kids run faster. But maybe the real reason they’re running faster is because they’re more excited, and excitement might be hiding somewhere in the mix. That hidden thing is like an unobserved confounder.

Let’s say we watch some kids eat ice cream and then race. We think ice cream causes them to run faster, but maybe it's actually their excitement that does the trick. Excitement could be something we don’t see, like a hidden friend helping them out.

Now imagine drawing this on paper with arrows showing how things connect. That’s what we call a Directed Acyclic Graph, or DAG for short. It helps us map out these invisible helpers and see where our understanding might be off track.

If we know excitement is hiding, we can find a way to bring it into the light, maybe by watching how excited kids are before they eat ice cream. This helps us tell if it’s really the ice cream or just the joy of being excited that makes them run faster. Using DAGs lets us do this even with tricky hidden helpers.