[digitalzombie]

I tried reading Pearl once. I couldn't get over his tone.

Andrew Gelman summarize it pretty nicely his take on it.

Coming from a statistic background, casual inference is a growing thing now and several government sponsor research have been pushing for it.

Casual inference from statistic point of view is base on missing data, basically Rudin stuff. It's pretty dang interesting to me. I'm sure there are many ways of looking at the same thing. Linear regression you can look at it in more of a optimal math problem with cost function or you can look at it in statitic using maximum likelihood estimation. Both have it's pro and con, with MLE you get a confidence interval. In my bias opinion I feel that statistic is only about data and it's a great domain for casual inference.

There's no need to put a field down to make yours better. But if it's constructive criticism (pro/con, contrast) I think it make both fields better. Pearl attitude is off putting when you try to read his stuff. We're all human and have vary degree of ego, if you're going to try to convince us that do calculus and your ways is better be objective about it or word things better. If you don't want to convince people then just be blunt as hell.