| > The catch is that, whether explicitly or implicitly, you must make assumptions in the first place about the directions of causality among the variables. That right there is the headline to me. Compare this with the blurb in the dust jacket of the book: > "Correlation is not causation." This mantra, espoused by scientists for more than a century, led to a virtual prohibition on causal talk. Today, that taboo is dead. The causal revolution, led by AI researcher Judea Pearl and his colleagues, has cut through years of confusion about the nature of knowledge and established the study of causality at the center of scientific inquiry. This all but claims that these new causal tools have found a solution to the problem of "correlation is not causation." But they have done no such thing: there is no new technique offered here for establishing causation in any better or easier way than the RCT of yore. If you get rid of the warning "correlation is not causation" and focus everyone's attention on all the exciting inferences you can make when you assume causation, I'm worried that the end result is a lot of bad science. |
But this, is the definition of science: you make models on top of hypothesis that you assume, you see how existing data fits your model, and then you make falsifiable predictions based on your model.
Studying data without any (causal) model of what's happening is just collecting statistically significant trivia. It is research, for sure, but that's not enough to make it science. But hey, at least you published something.