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Comment by nil-sec | Hacker News Reader

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.

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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.

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

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).

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