Wow that's crazy I listened to Elon musk  being interviewed by Dwarkesh and I came out of that thinking data centers in space at scale seemed a bit absurd. This has updated me a little that maybe it's more plausible than I thought.

Thanks so much @mal_graham🔸  that's the web page appreciate that! I understand that the WIT doesn't do all the work, but I think it was reasonable for me to assume that they were the major contributors given that they have been the team publishing the cross-cause work up to now, and were the team linked from the page.

Thanks Marcus for the reply. First this criticism is specifically about potential preferences and bias of the researchers. With a project like this with perhaps hundreds of junctures which require subjective decisions, I think that's a reasonable discussion to have. I don't think it's fair to ask me shift the ground and ask to discuss the research methodology,  I'm sure there'll be plenty of great discussion about that. My point was purely concerns about  potential researcher bias. 

I completely agree with your 3 points, and specifically that there are larger areas of concern than the cause area orientation of researchers - but I still think it's somewhat important .

This comment seems to twist the point I was trying to make "More broadly, I would not consider Claude’s opinions about the priorities of our researchers to be a good method of gauging the quality of our work" I was not questioning the quality of your work, I think your work is extremely high quality. Yet even the highest quality work can go in different directions, based on the assumptions and biases of the researchers who generate it. In many imprecise fields like development, economics and political science, the highest quality researchers can argue opposite sides. Stiglitz and Friedman both produced high quality work which often ended up at almost opposite conclusions. I would put cross-cause categorisation in a similar-ish category due to the wealth of assumptions required and uncertainties to be reckoned with. So in research like these yes, I think the make-up of the research team is important and open to scrutiny outside of objective criticism of the work itself. You might disagree with me on this one. Assuming people's backgrounds is open for discussion, I think a Claude analysis of people's previous work is reasonable if obviously very imprecise and yes potentially misleading.

I could be wrong here, but I feel like I may have been baited-and-switched a little on who are the major contributors here.. I went to your website and just searched the cross-cause prioritisation team, finding a page with photos and bios. You've said that the researchers I listed were not the principal researchers -  then who were? That Carmen van Schoubroeck led the work isn't listed anywhere I don't think. 

I agree with the direction but not the strength of this comment "I can never tell you with certainty no one who worked on this was not subtly unconsciously biased" and agree with your opening statement more "I think there is not a single EA organization I would consider unbiased on this question, including ourselves" We are all biased, often more than we think. Most of us (myself included) have a cess-pit of opinions and angles, despite our best efforts to the contrary. I think within EA we often overrate how objective we are, and we can only have so much objectivity based on our past experience and worldview built up over time. This is why I think for a cross-cause prioritisation exercise, it is helpful to start with a team with a wide range of prior opinions or perhaps very uncertain ones.

I like the idea of a 5th option "I don't know", but adding weights adds too much complexity for a public question stream I think. For the moral parliament or something deeper like that makes more sense to me

It's an interesting one Vasco. I prefer the current questions to uncertainty questions which I think are more intuitive for most people. I think it's important to lean towards questions which are easier to understand, rather than the "best theoretical" questions to help differenciate. Not everyone thinks as deeply as you about these things, and I like the accessibility of the current questions.

I think this is important work, but I want to flag my biggest concern about the process - the imbalance of the backgrounds of the research team and therefore potential for bias. I asked Claude to rank their areas of interest and prior work, and it shows a heavy bent towards Animal Welfare and Global Catastrophic risk.
 

Half of the research team have been strong advocates for Animal Welfare work in the past, while none of the team seems to have a special interest in GHD. Two of the team were deeply involved in the Animal Moral Weights project itself.

I think the work is impressive, and it's great there is a new fund, however I think a cross-cause research prioritisation team like this ideally would have been more balanced in makeup. Research team balance on prior opinions is especially important when the project relies on many assumptions and requires subjective assessments at many junctures. In addition potential conflicts of interest here should probably be stated on the website and in a post like this. Now that there is donation money directly involved, I feel the stakes are higher than when the situation was purely research (although the research alone is very influential).

A couple of other less important declarations perhaps could have been made as well. Given that  Lead Exposure Action Fund was the only specific GHD intervention selected, they perhaps should have mentioned that they have been commissioned to do research on lead exposure before, and also that a previous RP researcher is now a program associate at LEAF. The RP CEO was also previously a fund manager on the EA animal Welfare fund which allocates 13% of this fund's money.

All think tanks will have bias to some extent (Political think tanks are even rated on a spectrum) and I think its important to consider this and state where there might be potential bias.

I think It wouldn't cost much at all to make forward a pretty robust cost-effectiveness model for a CHW which rolls out a wide range of interventions. (I think Living Goods +- others might well have decent models already here?). I think you could even build this yourself? Some of the package would be easy to do because data is there (malaria, diarrhoea, pneumonia treatment, family planning antenatal care), while screenings and referrals are much harder to quantify and might have to be left out of the analysis pending better data.

I agree the bet argument is pretty good if the goal is government adoption. Regardless of the nuance of cost-effectiveness, CHWs will always be more cost-effective than most health things govt. could do and it would likely displace less cost-effective things. Unfortunately I don't think EA funders have seriously considered scale through govt. something worth chasing as a bet, but I really like the idea.

Thanks @Madeleine Ballard. As you know I'm a big fan of the Pro-CHW movement and all the work you do. I agree facility care can't reach everyone. I agree we need pro CHWs across Africa and like you say CHWs are often the main people doing the work facilitating EA funded vertical programs like mosquito nets and deworming. Sometimes this platform isn't recognised, and the cost borne by governments and donors who support these CHWs isn't appreciated properly in calculations - which I think I've pointed out to GiveWell before.

I also agree that EA doesn't pay nearly enough attention to gradual government adoption of the most cost-effective interventions, and I hope we're going to see more discussion about this in the near future.

When looking at RP's report, this was what I said in context.

"I agree that CHWs are an essential, scalable part of many LMIC health systems. CHWs however are touted to be very cost-effective when they often aren’t. This statement made my eyes pop a little. “Many studies have found CHW programs to be cost-effective by various metrics. In Mozambique, the annual cost per beneficiary was just $47.12. 

That number $47.12 I quoted from the RP report is quite different from $10 you've quoted as the median cost per capita here and doesn't scream cost-effectiveness. The report which  your $10 number came from wasn't out at the time I wrote that response. $10 per beneficiary still seems high to me but it's hard to compare to other interventions (see below)

One thing I'm confused about, is that I don't think a cost as low as $0.59 per capita is possible for a salaried CHW. How did the researchers get to this number? Trying to be as conservative as possible - here's my sense check. The ceiling for the no. of people a CHW can cover on a door-to-door model is perhaps 800. A lower end CHW salary might be $40 a month. Then we add commodity and supervision costs (excluding digital/connectivity costs). I've estimated this at around 20 people tested for malaria, 10 given treatment for malaria and 5 each treated for pneumonia and diarrhoea. Often CHWs would treat more people than this, which would mean higher costs.

- Salary (12x40) = $480 
- Malaria tests 20 a month (20 x $0.5 x 12 months ) = $120
- Malaria treatment 10 a month (10 x $0.3 x 12) = $36
- Pneumonia treatment 5 a month (5 x $0.5 x 12) = $30
- Diarrhoea ORS + zinc 5 a month (5 x $0.5 x 12) = $30
- Supervision ? $60 a year

= $756 / 800 people = $0.95 per capita per year under pretty conservative assumptions. I might be missing something here though!

Also "Per capita cost" and "Cost per beneficiary" are useful metrics for governments to decide if CHWs are affordable/cheap enough, but not useful metrics for cost-effectiveness comparisons. What can these metrics be compared to? We can't compare this metric to any other intervention. I've considered we could use a metric such as "cost of universal primary care per-capita" which would combine the cost of CHWs + other services, which could then be compared to facility only based models + extra transport costs. This would be tricky though... 

For CHWs a more straightforward "Cost per DALYs averted" would be more useful to compare with other EA funded interventions, or a more thorough GiveWell style CEA. 

Also on this point "Any cost-effectiveness analysis that attributes the full fixed cost of that worker to one of those service lines is going to produce a misleading number." I don't think Givewell attributes the full fixed cost of CHWs in CEAs. They might apportion some of the cost, or perhaps none. I often have the opposite criticism that they don't always appreciate the underlying cost of government health workers used in mass-distribution campaigns. I could be wrong here though I haven't looked closely recently.

Again I'm a huge fan of CHWs and your work and I believe in your mission. I'm just challenging whether your numbers make sense, and I think we need more than per-capita/per-beneficiary numbers to compare CHWs to other cost-effective interventions under EA frameworks.