Practical advice gives you a suggestion on what to do that will be good in some contexts, and bad in others. A causal model allows you to still still make accurate predictions in multiple unforeseen contexts, allows counterfactual modeling and thereby the modeling of interventions. There's a world of difference between the two. And I find a world of difference between people who think in terms of one or the other.
Today's post, Practical Advice Backed By Deep Theories was originally published on 25 April 2009. A summary (taken from the LW wiki):
Practical advice is genuinely much, much more useful when it's backed up by concrete experimental results, causal models that are actually true, or valid math that is validly interpreted. (Listed in increasing order of difficulty.) Stripping out the theories and giving the mere advice alone wouldn't have nearly the same impact or even the same message; and oddly enough, translating experiments and math into practical advice seems to be a rare niche activity relative to academia. If there's a distinctive LW style, this is it.
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