In one of the social media discussions about causal inference the suggestion was made that predictive models are all you need: a good predictive model gives you all the conditional distributions you could want, and you don’t need any special causal inference stuff. I think there’s something to this point of view, but there are a few limitations. The first is that causal inference theory (eg causal graphs) is useful for deciding what variables you need that you don’t have.
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