(a) It’s definitely fairly arbitrary, but the way I find it useful to think about it is that causes are problems, and you can break them down into:
High-level cause area: The broadest possible classification, like (i) problems that primarily affect humans in the here and now; (ii) problems that affect non-human animals; (iii) problems that primarily affect humans in the long run; and (iv) meta problems to do with EA itself.
Cause Area: High-level cause domains (e.g. neartermist human problems) can then be broken down into various intermediate-level cause areas (e.g. global disease and poverty → global health → communicable diseases → vector-borne diseases → mosquito borne diseases) until they reach the narrowest, individual cause level)
Cause: At the bottom, we have problems that are defined in the most narrow way possible (e.g. malaria).
In terms of what level cause prioritization research should focus on—I’m not sure if there’s an optimal level to always focus on. On the one hand, going narrow makes the actual research easier; on the other, you increase the amount of time needed to explore the search space, and also risk missing out on cross-cause solutions (e.g. vaccines for fungal diseases in general and not just, say, candidiasis).
(b) I think Michael Plant’s thesis had a good framing of the issue, and at the risk of summarizing his work poorly, I think the main point is that if causes are problems then interventions are solutions, and since we ultimately care about solving problems in a way that does the most good, we can’t really do cause prioritization research without also doing intervention evaluation.
The real challenge is identifying which solutions are the most effective, since at the shallow research stage we don’t have the time to look into everything. I can’t say I have a good answer this challenge, but in practice I would just briefly research what causes there are, and choose what superficially seems like the most effective. On the public health front, where the data is better, my understanding is that vaccines are (maybe unsurprisingly) very cost-effective, and same for gene drives.
(a) It’s definitely fairly arbitrary, but the way I find it useful to think about it is that causes are problems, and you can break them down into:
High-level cause area: The broadest possible classification, like (i) problems that primarily affect humans in the here and now; (ii) problems that affect non-human animals; (iii) problems that primarily affect humans in the long run; and (iv) meta problems to do with EA itself.
Cause Area: High-level cause domains (e.g. neartermist human problems) can then be broken down into various intermediate-level cause areas (e.g. global disease and poverty → global health → communicable diseases → vector-borne diseases → mosquito borne diseases) until they reach the narrowest, individual cause level)
Cause: At the bottom, we have problems that are defined in the most narrow way possible (e.g. malaria).
In terms of what level cause prioritization research should focus on—I’m not sure if there’s an optimal level to always focus on. On the one hand, going narrow makes the actual research easier; on the other, you increase the amount of time needed to explore the search space, and also risk missing out on cross-cause solutions (e.g. vaccines for fungal diseases in general and not just, say, candidiasis).
(b) I think Michael Plant’s thesis had a good framing of the issue, and at the risk of summarizing his work poorly, I think the main point is that if causes are problems then interventions are solutions, and since we ultimately care about solving problems in a way that does the most good, we can’t really do cause prioritization research without also doing intervention evaluation.
The real challenge is identifying which solutions are the most effective, since at the shallow research stage we don’t have the time to look into everything. I can’t say I have a good answer this challenge, but in practice I would just briefly research what causes there are, and choose what superficially seems like the most effective. On the public health front, where the data is better, my understanding is that vaccines are (maybe unsurprisingly) very cost-effective, and same for gene drives.