[tanana]

Correcting for confounding is probably the hardest thing to do when you are building statistical models. There is no true consensus with regard to how you should go about correcting for confounding.

Did you know that if you correct wrongly for a confounder, you actually introduce bias?

As a very modest medical researcher myself, I have become very careful about the conclusions I make with any model I make. While it is very appealing to make conclusions about causation, they are very often wrong.

For more information, I think this might interest you: https://www.hsph.harvard.edu/miguel-hernan/causal-inference-...

[dqpb]

The Book of Why

https://www.amazon.com/Book-Why-Science-Cause-Effect