Prediction vs causation in a ML project 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?
ML questions are concerned mainly around predictions, but we can extrapolate to (in a certain sence) causality.

First of all these are two different modelling approaches:

Causal inference is focused on knowing what happens to $Y$ when you change $X$.  Prediction is focused on knowing the next $Y$ given $X$ 

Some of the current causal approaches are randomised testing, do-calculus etc...

So how can we extrapolate to causal inference of standard predictive ML models?

Counterfactual Explanations we can simulate counterfactuals for predictions of machine learning models where we simply change the feature values of an instance before making the predictions and we analyze how the prediction changes. Read more about it here, and there is python library called alibi that implements it.