I have been wondering about this too. Never saw a single situation where causal modeling was practically applied.
I am thinking whether potentially there is a fundamental flaw here.
If you don't know what causes what but just observe the correlation then most likely you will never find out what is the underlying cause because the entire causation is so complicated or hard to find or lacking further data that it's just not feasible to figure it out.
If you know what causes what you can build your model accordingly. But in such a situation, the causality is usually not "perfect". For example: If A causes B every single time when B occurs, then B does not truly give you any further information at all, because apparently all information must be contained in A. If, however, B does occur only with a certain probability when A occurs then you again are back to not knowing exactly how causality works, and there are unkonwn factors you cannot account for.
I have been wondering about this too. Never saw a single situation where causal modeling was practically applied.
I am thinking whether potentially there is a fundamental flaw here.
If you don't know what causes what but just observe the correlation then most likely you will never find out what is the underlying cause because the entire causation is so complicated or hard to find or lacking further data that it's just not feasible to figure it out.
If you know what causes what you can build your model accordingly. But in such a situation, the causality is usually not "perfect". For example: If A causes B every single time when B occurs, then B does not truly give you any further information at all, because apparently all information must be contained in A. If, however, B does occur only with a certain probability when A occurs then you again are back to not knowing exactly how causality works, and there are unkonwn factors you cannot account for.
Don't know, just some thoughts.