u/good_hugs

In science all through our education we are told that correlation doesn't equal causation and then when it comes to machine learning we are taught to choose models by how they perform, how well they fit to data and can predict outcomes.

 

Is this not just a really fancy way of finding correlations?

 

It's obvious but I don't feel like this is reckoned with appropriately.

 

To be clear I am not anti ML or AI just a bit confused about how we are using these tools.

If anyone has some thoughts about this I would be very interested!

Or an example of how you have balanced using models and more mechanistic approaches.

 

Thank you 😄