I am performing a classification task and was able to identify significant predictors (important features using Random Forest) that can help separate the classes or influence the outcome.
But I read online that
prediction models are not causal models.
Let's say if my prediction model says that
Age is one of the significant factors that influence outcome (death), how can I prove that
Age is the cause of death.
I read that any intervention/change on strong predictors of your models, will not necessarily impact the outcome.
How can I find out the list of factors that really cause change in the outcome?
Currently what I do is run a RF model to identify the important features and communicate that these are the
top 5 features that seem to influence the outcome.
How can I prove that it is causation and not just correlation?