Prediction vs causation in a ML project
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.