[littlestymaar]

> 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.

[haberman]

Sure, but what does Pearl bring to the table here? The idea of making falsifiable predictions long predates the "Causal Revolution." The warning of "correlation is not causation" still seems as relevant as ever.

[nil-sec]

For one it lets you avoid controlling for the wrong variables and causing e.g. spurious correlations by doing so. In fact this is one of the best examples of why a causal model is necessary, because without one you can easily end up with a correlation that doesn’t exist as is illustrated quite nicely in his book.

[haberman]

Do you mean that the new techniques will (1) help you prove which variables should not be controlled for, or that they will (2) help you more clearly describe your causal assumptions, so that you can more easily recognize which variables should not be controlled for according to your assumptions?

If you mean (2), I can't really disagree: explicitly specifying your causal assumptions through a DAG seems like a clarifying step in specifying a model.

If you mean (1), then I must be missing something because I'm not seeing that this set of tools can do that.

My worry is that (2) is mistaken for (1), and that writing down a causal model is conflated with proving that it is true.

[nil-sec]

For a given causal model it is (1) in my understanding.

[haberman]

But "for a given causal model" precisely means "given a set of statements about what causes what." Those statements must be either proved or assumed.

If they are already proved, they don't need to be proved further per (1).

If they are not already proved, then they are just assumptions and we are talking about (2).