In fact, I’d go further and argue that explainatory modeling is just a mistaken approach to predictive modeling. Why do we want to understand how things work? To make better decisions. But we don’t really need to understand how things work to make better decisions, we just need to know how things will react to our what we do.
The word “react” here is a causal term. To predict how things will “react” we need some sort of causal model.
What makes predictive modeling a better idea is that it also allows us to find factors that are not causal, but still useful.
Usefulness is also a causal notion. X is useful if it causes a good outcome. If X doesn’t cause a good outcome, but is merely correlated with it, it isn’t useful.
I’m inclined to 180 on the original statements there and instead argue that predictive modelling works because, as Pearl says, “no correlation without causation”. Then an important step when basing decisions on predictive modelling is verifying that the intervention has not cut off the causal path we depended on for decision-making.
To push back a little:
The word “react” here is a causal term. To predict how things will “react” we need some sort of causal model.
Usefulness is also a causal notion. X is useful if it causes a good outcome. If X doesn’t cause a good outcome, but is merely correlated with it, it isn’t useful.
Oh, these are good objections. Thanks!
I’m inclined to 180 on the original statements there and instead argue that predictive modelling works because, as Pearl says, “no correlation without causation”. Then an important step when basing decisions on predictive modelling is verifying that the intervention has not cut off the causal path we depended on for decision-making.
Do you think that would be closer to the truth?