Micro-interventions – deworming, cataract surgery, mental health apps, and cement flooring
Meso-interventions – lead regulation, immigration policy, and improving access to pain relief
Macro-interventions – building a £10 million portfolio of philanthropic funding opportunities
re: comparing the metrics apples for apples, (my impression of) their point is that they simply aren’t commensurable, because (to quote the relevant part of the OP)
The advantages of SWB over alternatives are fourfold. … (2) SWB is based on self-reports by the affected individuals whereas Q/DALYs rely on flawed predictions about how good or bad we think a malady will be for ourselves or others. (3) Using SWB will reveal previously under-captured benefits, such as it has already done for psychotherapy. …
It doesn’t seem very practical to come up with Q/DALY-to-WELLBY “conversion rates” for all 440 health states in the GBD to adjust for the over/underweighting due to affective forecasting.
Thanks for this! The reason i brought up interventions that they would want to fund is that I figured that they were interested in improving the WELLBY metric. If they are planning on being a regranter, then thats a whole different story to me.
I agree that they might very well be incommensurable. However, I suspect that different organizations will want to use different metrics, and someone like OpenPhil or one day GiveWell might have to be able to compare the two somehow. d
You’re right that metric conversions are of interest to some orgs; for instance GiveWell and HLI both use moral weights to convert between averting death and increasing income. Other orgs don’t; for instance TLYCS looks at 4 core outcomes (lives saved, life-years added, income gained, carbon removed) and maintain them separately, and Open Phil have their “worldview buckets”. I lean towards converting metrics mostly for the reasons Nuno writes about, but I’m also swayed by Holden’s argument that cluster thinking (a main driver of worldview diversification) is more robust w.r.t. handling Knightian uncertainty, so I’m left unsure which approach (“to convert or not to convert?”) is best for EA as a whole.
Re: next steps, maybe check out their page Our story so far, scroll down to ’2022′:
re: comparing the metrics apples for apples, (my impression of) their point is that they simply aren’t commensurable, because (to quote the relevant part of the OP)
It doesn’t seem very practical to come up with Q/DALY-to-WELLBY “conversion rates” for all 440 health states in the GBD to adjust for the over/underweighting due to affective forecasting.
Thanks for this!
The reason i brought up interventions that they would want to fund is that I figured that they were interested in improving the WELLBY metric. If they are planning on being a regranter, then thats a whole different story to me.
I agree that they might very well be incommensurable. However, I suspect that different organizations will want to use different metrics, and someone like OpenPhil or one day GiveWell might have to be able to compare the two somehow. d
No worries (:
You’re right that metric conversions are of interest to some orgs; for instance GiveWell and HLI both use moral weights to convert between averting death and increasing income. Other orgs don’t; for instance TLYCS looks at 4 core outcomes (lives saved, life-years added, income gained, carbon removed) and maintain them separately, and Open Phil have their “worldview buckets”. I lean towards converting metrics mostly for the reasons Nuno writes about, but I’m also swayed by Holden’s argument that cluster thinking (a main driver of worldview diversification) is more robust w.r.t. handling Knightian uncertainty, so I’m left unsure which approach (“to convert or not to convert?”) is best for EA as a whole.
Interesting stuff and out of my depth! Seems like something I should nerd out on for awhile :) Anywhere you suggest I could start?