On the caffeine/longevity question ⇒ would ought be able to factorize variables used in causal modeling? (eg figure out that caffeine is a mTOR+phosphodiesterase inhibitor and then factorize caffeine’s effects on longevity through mTOR/phosphodiesterase)? This could be used to make estimates for drugs even if there are no direct studies on the relationship between {drug, longevity}
Yes—causal reasoning is a clear case where decomposition seems promising. For example:
How does X affect Y?
What’s a Z on the causal path between X and Y, screening off Y from X?
What is X’s effect on Z?
What is Z’s effect on Y?
Based on the answers to 2 & 3, what is X’s effect on Y?
We’d need to be careful about all the usual ways causal reasoning can go wrong by ignoring confounders etc
Thanks for the long list of research questions!
Yes—causal reasoning is a clear case where decomposition seems promising. For example:
We’d need to be careful about all the usual ways causal reasoning can go wrong by ignoring confounders etc