Thank you for the link, that is a very good presentation and it is good to see that ML people are thinking about these things.
There certainly are ML algorithms that are designed to make the second kind of predictions, but generally they only work if you have a correct causal model
It is possible that there are some ML algorithms that try to discover the causal model from the data. For example, /u/IlyaShpitser works on these kinds of methods. However, these methods only work to the extent that they are able to discover the correct causal model, so it seems disingenious to claim that we can ignore causality and focus on “prediction”.
Thank you for the link, that is a very good presentation and it is good to see that ML people are thinking about these things.
There certainly are ML algorithms that are designed to make the second kind of predictions, but generally they only work if you have a correct causal model
It is possible that there are some ML algorithms that try to discover the causal model from the data. For example, /u/IlyaShpitser works on these kinds of methods. However, these methods only work to the extent that they are able to discover the correct causal model, so it seems disingenious to claim that we can ignore causality and focus on “prediction”.