In most private sector situations you will not care about causality
In practice, despite typical language use, people are much more often interested in well understood impact, rather than (well understood) causality.
From an academic point of view, it is very interesting to know:
If I do A, because of that the outcome will be B
But from a practical point of view, in nearly all situations the following is what people really want to know:
If I do A, the outcome will be B
Sure you may be interested in the impact of A, but whether it is truely the cause, or whether there is a hidden cause that just happens to create this correlation is usually not that interesting.
Note on limitations
You may think: ok, but if we don't know that A causes B, then it is very risky to work on that assumption.
This is true in a way, but again in practice you will just worry about: Will it work, or are there exceptions?
To illustrate this, you may note that this situation:
If I do A, in situation X, because of A the outcome will be B and because of X the outcome will deviate by delta
Is not much more helpfull than this situation (assuming you can quantify the impacts equally):
If I do A, in situation X, the outcome will be B and the outcome will deviate by delta
Simple example: Correlation to cause
- A: Replenish engine oil
- B: Reduced brake faillure
- C: Car checkup
The logic: C always causes A and B
Resulting relation: If A goes up, B goes up but there is no causal relation between A and B.
My point: You can model the impact of A on B. A does not cause B, but the model will still be correct, and if you have information about A, you will have information about B.
The person interested in brake faillure with information about A will just care about knowing the relation of A to B, and only care whether the relation is correct, regardless of whether this relation is causal or not.