Confounding adjustment is one of the most common phrases in epidemiology and observational research. It is often treated as proof that a study has handled bias and moved closer to a causal answer. But what if adjustment is creating more confidence than the data actually deserve? 

In this episode, we break down why confounding adjustment often fails, how poorly measured or incorrectly chosen variables leave bias behind, and why “adjusted” is not the same thing as “causal.” If you read or produce observational research, this is an essential concept to understand.

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