Background: Earlier this year, I attended a great presentation by Natália Mendoça about experience sampling. Here’s the deck from her presentation.
A takeaway from the presentation was that QALYs are constructed in a way that skews cause prioritization towards particular causes. Alternative metrics have different skews, so using an alternative metric could lead to very different cause prioritization.
For example, under the QALY framework, one year with “some problems walking about” is considered to be about as bad as one year with “moderate anxiety or depression.”
For anyone who’s had some experience with depression or anxiety, as well as with “some problems walking about,” it should be obvious that moderate depression or anxiety are (much) worse than moderate mobility problems, pound for pound. (Please reach out if you disagree with this, I want to pick your brain if you do.)
A takeaway from that post is that rolling out an experience-sampling framework seems very tractable.
This research direction seems like plausibly a high priority for EA, as basing cause prioritization on a different metric could lead to notably different priority causes.
In particular, experience sampling appears to give a higher weight to mental health disorders than QALYs does, so it’s plausible that under an experience-sampling framework, mental health interventions would be higher priority than global health interventions.
Given the potential magnitude of this delta in prioritization (between the experience-sampling & QALY frameworks), it’s surprising to me that there’s not been more interest in investigating alternatives to the QALY in the EA community.
To be clear, I’m not claiming that the experience-sampling method is superior to QALYs. I’m claiming that it is constructed in an equally plausibly way to the QALY, and that it probably results in drastically different cause prioritization. One potentially robust path forward could be to split the difference between prioritization implied by QALYs and prioritization implied by experience sampling.
[Disclosure: In February 2019, I corresponded about the experience-sampling idea with Alex Foster of the EA Meta Fund. He said my points were “certainly quite compelling,” but the correspondence fell off.
I heard later from another source that the EA Meta Fund didn’t end up getting excited about the idea, though they didn’t say why not.]
[Question] Why does EA use QALYs instead of experience sampling?
Background: Earlier this year, I attended a great presentation by Natália Mendoça about experience sampling. Here’s the deck from her presentation.
A takeaway from the presentation was that QALYs are constructed in a way that skews cause prioritization towards particular causes. Alternative metrics have different skews, so using an alternative metric could lead to very different cause prioritization.
For example, under the QALY framework, one year with “some problems walking about” is considered to be about as bad as one year with “moderate anxiety or depression.”
For anyone who’s had some experience with depression or anxiety, as well as with “some problems walking about,” it should be obvious that moderate depression or anxiety are (much) worse than moderate mobility problems, pound for pound. (Please reach out if you disagree with this, I want to pick your brain if you do.)
An alternative metric to QALYs is called experience sampling. Last month, Natália posted about experience sampling on the Forum. The post was moderately upvoted, though no one commented on it.
A takeaway from that post is that rolling out an experience-sampling framework seems very tractable.
This research direction seems like plausibly a high priority for EA, as basing cause prioritization on a different metric could lead to notably different priority causes.
In particular, experience sampling appears to give a higher weight to mental health disorders than QALYs does, so it’s plausible that under an experience-sampling framework, mental health interventions would be higher priority than global health interventions.
Given the potential magnitude of this delta in prioritization (between the experience-sampling & QALY frameworks), it’s surprising to me that there’s not been more interest in investigating alternatives to the QALY in the EA community.
To be clear, I’m not claiming that the experience-sampling method is superior to QALYs. I’m claiming that it is constructed in an equally plausibly way to the QALY, and that it probably results in drastically different cause prioritization. One potentially robust path forward could be to split the difference between prioritization implied by QALYs and prioritization implied by experience sampling.
[Disclosure: In February 2019, I corresponded about the experience-sampling idea with Alex Foster of the EA Meta Fund. He said my points were “certainly quite compelling,” but the correspondence fell off.
I heard later from another source that the EA Meta Fund didn’t end up getting excited about the idea, though they didn’t say why not.]