You just lost all readers who don’t know what a joint distribution is
I think this is generally useful and fair feedback, but I don’t think the above is quite fair, in the sense that people who don’t know what a joint distribution is should not be reading this.
edit: Although, you know, I have to wonder. It is probably possible to teach causality without going into probability first. A computer program is a causal structure. When we stop program execution via a breakpoint and set a variable, that is an intervention. Computer programmers learn that stuff very early, and it is central to causality. It seems that the concepts end up being useful to statisticians and the like, not computer programmers, so probability theory gets put in early.
Anatoly, you mentioned earlier that you were looking for a readable intro to causal inference. I would be interested in what sorts of things you would be looking for in such an intro, in case I ever find the time to “write something.”
I think this is generally useful and fair feedback, but I don’t think the above is quite fair, in the sense that people who don’t know what a joint distribution is should not be reading this.
edit: Although, you know, I have to wonder. It is probably possible to teach causality without going into probability first. A computer program is a causal structure. When we stop program execution via a breakpoint and set a variable, that is an intervention. Computer programmers learn that stuff very early, and it is central to causality. It seems that the concepts end up being useful to statisticians and the like, not computer programmers, so probability theory gets put in early.
Anatoly, you mentioned earlier that you were looking for a readable intro to causal inference. I would be interested in what sorts of things you would be looking for in such an intro, in case I ever find the time to “write something.”